Overview

Dataset statistics

Number of variables57
Number of observations3287
Missing cells1243
Missing cells (%)0.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.6 MiB
Average record size in memory1.1 KiB

Variable types

Categorical37
Numeric19
URL1

Warnings

Reality-TV has constant value "0" Constant
Game-Show has constant value "0" Constant
Film-Noir has constant value "0" Constant
director_name has a high cardinality: 1788 distinct values High cardinality
actor_2_name has a high cardinality: 2074 distinct values High cardinality
genres has a high cardinality: 688 distinct values High cardinality
actor_1_name has a high cardinality: 1430 distinct values High cardinality
movie_title has a high cardinality: 3223 distinct values High cardinality
actor_3_name has a high cardinality: 2404 distinct values High cardinality
plot_keywords has a high cardinality: 3119 distinct values High cardinality
num_critic_for_reviews is highly correlated with gross and 6 other fieldsHigh correlation
actor_3_facebook_likes is highly correlated with actor_2_facebook_likesHigh correlation
actor_1_facebook_likes is highly correlated with cast_total_facebook_likesHigh correlation
gross is highly correlated with num_critic_for_reviews and 5 other fieldsHigh correlation
num_voted_users is highly correlated with num_critic_for_reviews and 6 other fieldsHigh correlation
cast_total_facebook_likes is highly correlated with actor_1_facebook_likes and 1 other fieldsHigh correlation
num_user_for_reviews is highly correlated with num_critic_for_reviews and 4 other fieldsHigh correlation
actor_2_facebook_likes is highly correlated with actor_3_facebook_likes and 1 other fieldsHigh correlation
movie_facebook_likes is highly correlated with num_critic_for_reviews and 1 other fieldsHigh correlation
Family is highly correlated with AnimationHigh correlation
Animation is highly correlated with FamilyHigh correlation
Adventure is highly correlated with budget_yHigh correlation
budget_y is highly correlated with num_critic_for_reviews and 5 other fieldsHigh correlation
revenue is highly correlated with num_critic_for_reviews and 5 other fieldsHigh correlation
profit is highly correlated with num_critic_for_reviews and 5 other fieldsHigh correlation
num_critic_for_reviews is highly correlated with gross and 6 other fieldsHigh correlation
actor_3_facebook_likes is highly correlated with actor_1_facebook_likes and 2 other fieldsHigh correlation
actor_1_facebook_likes is highly correlated with actor_3_facebook_likes and 2 other fieldsHigh correlation
gross is highly correlated with num_critic_for_reviews and 6 other fieldsHigh correlation
num_voted_users is highly correlated with num_critic_for_reviews and 6 other fieldsHigh correlation
cast_total_facebook_likes is highly correlated with actor_3_facebook_likes and 2 other fieldsHigh correlation
num_user_for_reviews is highly correlated with num_critic_for_reviews and 6 other fieldsHigh correlation
budget_x is highly correlated with num_critic_for_reviews and 5 other fieldsHigh correlation
actor_2_facebook_likes is highly correlated with actor_3_facebook_likes and 2 other fieldsHigh correlation
Family is highly correlated with AnimationHigh correlation
Animation is highly correlated with FamilyHigh correlation
budget_y is highly correlated with num_critic_for_reviews and 5 other fieldsHigh correlation
revenue is highly correlated with num_critic_for_reviews and 6 other fieldsHigh correlation
profit is highly correlated with num_critic_for_reviews and 4 other fieldsHigh correlation
num_critic_for_reviews is highly correlated with num_voted_users and 2 other fieldsHigh correlation
actor_3_facebook_likes is highly correlated with cast_total_facebook_likes and 1 other fieldsHigh correlation
actor_1_facebook_likes is highly correlated with cast_total_facebook_likes and 1 other fieldsHigh correlation
gross is highly correlated with num_voted_users and 4 other fieldsHigh correlation
num_voted_users is highly correlated with num_critic_for_reviews and 3 other fieldsHigh correlation
cast_total_facebook_likes is highly correlated with actor_3_facebook_likes and 2 other fieldsHigh correlation
num_user_for_reviews is highly correlated with num_critic_for_reviews and 2 other fieldsHigh correlation
budget_x is highly correlated with gross and 2 other fieldsHigh correlation
actor_2_facebook_likes is highly correlated with actor_3_facebook_likes and 2 other fieldsHigh correlation
Family is highly correlated with AnimationHigh correlation
Animation is highly correlated with FamilyHigh correlation
budget_y is highly correlated with gross and 2 other fieldsHigh correlation
revenue is highly correlated with num_critic_for_reviews and 6 other fieldsHigh correlation
profit is highly correlated with gross and 1 other fieldsHigh correlation
revenue is highly correlated with profit and 4 other fieldsHigh correlation
profit is highly correlated with revenue and 5 other fieldsHigh correlation
Crime is highly correlated with ThrillerHigh correlation
Family is highly correlated with Animation and 3 other fieldsHigh correlation
actor_3_facebook_likes is highly correlated with actor_2_facebook_likes and 3 other fieldsHigh correlation
country is highly correlated with News and 2 other fieldsHigh correlation
News is highly correlated with country and 1 other fieldsHigh correlation
actor_2_facebook_likes is highly correlated with actor_3_facebook_likes and 1 other fieldsHigh correlation
Comedy is highly correlated with ThrillerHigh correlation
Thriller is highly correlated with Crime and 2 other fieldsHigh correlation
language is highly correlated with country and 1 other fieldsHigh correlation
Mystery is highly correlated with ThrillerHigh correlation
Short is highly correlated with duration and 1 other fieldsHigh correlation
movie_facebook_likes is highly correlated with num_voted_users and 1 other fieldsHigh correlation
gross is highly correlated with revenue and 7 other fieldsHigh correlation
Animation is highly correlated with Family and 2 other fieldsHigh correlation
num_voted_users is highly correlated with revenue and 8 other fieldsHigh correlation
content_rating is highly correlated with Family and 1 other fieldsHigh correlation
Adventure is highly correlated with profit and 4 other fieldsHigh correlation
duration is highly correlated with Short and 1 other fieldsHigh correlation
cast_total_facebook_likes is highly correlated with actor_3_facebook_likes and 2 other fieldsHigh correlation
budget_y is highly correlated with revenue and 4 other fieldsHigh correlation
num_critic_for_reviews is highly correlated with movie_facebook_likes and 2 other fieldsHigh correlation
actor_1_facebook_likes is highly correlated with cast_total_facebook_likesHigh correlation
budget_x is highly correlated with country and 3 other fieldsHigh correlation
imdb_score is highly correlated with num_voted_usersHigh correlation
num_user_for_reviews is highly correlated with revenue and 4 other fieldsHigh correlation
Fantasy is highly correlated with FamilyHigh correlation
Horror is highly correlated with Reality-TV and 2 other fieldsHigh correlation
History is highly correlated with Reality-TV and 2 other fieldsHigh correlation
Action is highly correlated with Reality-TV and 2 other fieldsHigh correlation
Musical is highly correlated with Reality-TV and 2 other fieldsHigh correlation
Romance is highly correlated with Reality-TV and 2 other fieldsHigh correlation
Crime is highly correlated with Reality-TV and 2 other fieldsHigh correlation
Reality-TV is highly correlated with Horror and 28 other fieldsHigh correlation
Family is highly correlated with Reality-TV and 4 other fieldsHigh correlation
country is highly correlated with Reality-TV and 4 other fieldsHigh correlation
News is highly correlated with Reality-TV and 3 other fieldsHigh correlation
Music is highly correlated with Reality-TV and 2 other fieldsHigh correlation
Comedy is highly correlated with Reality-TV and 2 other fieldsHigh correlation
Biography is highly correlated with Reality-TV and 2 other fieldsHigh correlation
Thriller is highly correlated with Reality-TV and 2 other fieldsHigh correlation
language is highly correlated with Reality-TV and 3 other fieldsHigh correlation
Game-Show is highly correlated with Horror and 28 other fieldsHigh correlation
Documentary is highly correlated with Reality-TV and 2 other fieldsHigh correlation
Sport is highly correlated with Reality-TV and 2 other fieldsHigh correlation
Mystery is highly correlated with Reality-TV and 2 other fieldsHigh correlation
Short is highly correlated with Reality-TV and 3 other fieldsHigh correlation
Animation is highly correlated with Reality-TV and 4 other fieldsHigh correlation
War is highly correlated with Reality-TV and 2 other fieldsHigh correlation
content_rating is highly correlated with Reality-TV and 5 other fieldsHigh correlation
Western is highly correlated with Reality-TV and 2 other fieldsHigh correlation
Adventure is highly correlated with Reality-TV and 2 other fieldsHigh correlation
color is highly correlated with Reality-TV and 2 other fieldsHigh correlation
Sci-Fi is highly correlated with Reality-TV and 2 other fieldsHigh correlation
Drama is highly correlated with Reality-TV and 2 other fieldsHigh correlation
Film-Noir is highly correlated with Horror and 28 other fieldsHigh correlation
Fantasy is highly correlated with Reality-TV and 2 other fieldsHigh correlation
gross has 393 (12.0%) missing values Missing
plot_keywords has 102 (3.1%) missing values Missing
content_rating has 157 (4.8%) missing values Missing
budget_x has 265 (8.1%) missing values Missing
aspect_ratio has 213 (6.5%) missing values Missing
budget_x is highly skewed (γ1 = 51.50092352) Skewed
actor_2_name is uniformly distributed Uniform
movie_title is uniformly distributed Uniform
actor_3_name is uniformly distributed Uniform
plot_keywords is uniformly distributed Uniform
director_facebook_likes has 548 (16.7%) zeros Zeros
actor_3_facebook_likes has 51 (1.6%) zeros Zeros
facenumber_in_poster has 1386 (42.2%) zeros Zeros
actor_2_facebook_likes has 34 (1.0%) zeros Zeros
movie_facebook_likes has 1353 (41.2%) zeros Zeros
budget_y has 549 (16.7%) zeros Zeros
revenue has 764 (23.2%) zeros Zeros
profit has 435 (13.2%) zeros Zeros

Reproduction

Analysis started2021-08-09 02:51:29.813787
Analysis finished2021-08-09 02:52:43.990209
Duration1 minute and 14.18 seconds
Software versionpandas-profiling v3.0.0
Download configurationconfig.json

Variables

color
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing9
Missing (%)0.3%
Memory size225.3 KiB
Color
3196 
Black and White
 
82

Length

Max length16
Median length5
Mean length5.275167785
Min length5

Characters and Unicode

Total characters17292
Distinct characters16
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowColor
2nd rowColor
3rd rowColor
4th rowColor
5th rowColor

Common Values

ValueCountFrequency (%)
Color3196
97.2%
Black and White82
 
2.5%
(Missing)9
 
0.3%

Length

2021-08-09T10:52:44.253769image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-08-09T10:52:44.331918image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
color3196
92.9%
white82
 
2.4%
black82
 
2.4%
and82
 
2.4%

Most occurring characters

ValueCountFrequency (%)
o6392
37.0%
l3278
19.0%
C3196
18.5%
r3196
18.5%
246
 
1.4%
a164
 
0.9%
B82
 
0.5%
c82
 
0.5%
k82
 
0.5%
n82
 
0.5%
Other values (6)492
 
2.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter13686
79.1%
Uppercase Letter3360
 
19.4%
Space Separator246
 
1.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o6392
46.7%
l3278
24.0%
r3196
23.4%
a164
 
1.2%
c82
 
0.6%
k82
 
0.6%
n82
 
0.6%
d82
 
0.6%
h82
 
0.6%
i82
 
0.6%
Other values (2)164
 
1.2%
Uppercase Letter
ValueCountFrequency (%)
C3196
95.1%
B82
 
2.4%
W82
 
2.4%
Space Separator
ValueCountFrequency (%)
246
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin17046
98.6%
Common246
 
1.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
o6392
37.5%
l3278
19.2%
C3196
18.7%
r3196
18.7%
a164
 
1.0%
B82
 
0.5%
c82
 
0.5%
k82
 
0.5%
n82
 
0.5%
d82
 
0.5%
Other values (5)410
 
2.4%
Common
ValueCountFrequency (%)
246
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII17292
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o6392
37.0%
l3278
19.0%
C3196
18.5%
r3196
18.5%
246
 
1.4%
a164
 
0.9%
B82
 
0.5%
c82
 
0.5%
k82
 
0.5%
n82
 
0.5%
Other values (6)492
 
2.8%

director_name
Categorical

HIGH CARDINALITY

Distinct1788
Distinct (%)54.4%
Missing0
Missing (%)0.0%
Memory size252.2 KiB
Steven Soderbergh
 
14
Clint Eastwood
 
13
Woody Allen
 
12
Steven Spielberg
 
11
Ridley Scott
 
11
Other values (1783)
3226 

Length

Max length32
Median length13
Mean length13.04137511
Min length3

Characters and Unicode

Total characters42867
Distinct characters73
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1147 ?
Unique (%)34.9%

Sample

1st rowDan Trachtenberg
2nd rowTimothy Hines
3rd rowGil Junger
4th rowKevin Lima
5th rowRobert Moresco

Common Values

ValueCountFrequency (%)
Steven Soderbergh14
 
0.4%
Clint Eastwood13
 
0.4%
Woody Allen12
 
0.4%
Steven Spielberg11
 
0.3%
Ridley Scott11
 
0.3%
Peter Jackson11
 
0.3%
Ron Howard10
 
0.3%
Tim Burton10
 
0.3%
Shawn Levy10
 
0.3%
Kevin Smith9
 
0.3%
Other values (1778)3176
96.6%

Length

2021-08-09T10:52:44.597476image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
david116
 
1.7%
john91
 
1.3%
michael87
 
1.3%
paul62
 
0.9%
peter56
 
0.8%
james55
 
0.8%
robert49
 
0.7%
scott47
 
0.7%
chris37
 
0.5%
mike37
 
0.5%
Other values (2294)6188
90.7%

Most occurring characters

ValueCountFrequency (%)
e3992
 
9.3%
a3551
 
8.3%
3538
 
8.3%
n3074
 
7.2%
r2902
 
6.8%
i2481
 
5.8%
o2457
 
5.7%
l2023
 
4.7%
t1555
 
3.6%
s1401
 
3.3%
Other values (63)15893
37.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter32123
74.9%
Uppercase Letter6983
 
16.3%
Space Separator3538
 
8.3%
Other Punctuation174
 
0.4%
Dash Punctuation49
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e3992
12.4%
a3551
11.1%
n3074
9.6%
r2902
 
9.0%
i2481
 
7.7%
o2457
 
7.6%
l2023
 
6.3%
t1555
 
4.8%
s1401
 
4.4%
h1227
 
3.8%
Other values (30)7460
23.2%
Uppercase Letter
ValueCountFrequency (%)
S666
 
9.5%
J608
 
8.7%
M605
 
8.7%
R479
 
6.9%
C476
 
6.8%
D453
 
6.5%
B453
 
6.5%
A387
 
5.5%
P340
 
4.9%
G333
 
4.8%
Other values (19)2183
31.3%
Other Punctuation
ValueCountFrequency (%)
.158
90.8%
'16
 
9.2%
Space Separator
ValueCountFrequency (%)
3538
100.0%
Dash Punctuation
ValueCountFrequency (%)
-49
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin39106
91.2%
Common3761
 
8.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
e3992
 
10.2%
a3551
 
9.1%
n3074
 
7.9%
r2902
 
7.4%
i2481
 
6.3%
o2457
 
6.3%
l2023
 
5.2%
t1555
 
4.0%
s1401
 
3.6%
h1227
 
3.1%
Other values (59)14443
36.9%
Common
ValueCountFrequency (%)
3538
94.1%
.158
 
4.2%
-49
 
1.3%
'16
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII42780
99.8%
Latin 1 Sup87
 
0.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e3992
 
9.3%
a3551
 
8.3%
3538
 
8.3%
n3074
 
7.2%
r2902
 
6.8%
i2481
 
5.8%
o2457
 
5.7%
l2023
 
4.7%
t1555
 
3.6%
s1401
 
3.3%
Other values (46)15806
36.9%
Latin 1 Sup
ValueCountFrequency (%)
é25
28.7%
ö15
17.2%
á12
13.8%
ó8
 
9.2%
å6
 
6.9%
í4
 
4.6%
ñ3
 
3.4%
ä2
 
2.3%
ç2
 
2.3%
É2
 
2.3%
Other values (7)8
 
9.2%

num_critic_for_reviews
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct520
Distinct (%)16.0%
Missing29
Missing (%)0.9%
Infinite0
Infinite (%)0.0%
Mean168.1685083
Minimum1
Maximum813
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size51.4 KiB
2021-08-09T10:52:44.722448image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile11
Q171
median142
Q3231.75
95-th percentile432.15
Maximum813
Range812
Interquartile range (IQR)160.75

Descriptive statistics

Standard deviation130.9393784
Coefficient of variation (CV)0.778620086
Kurtosis1.846966342
Mean168.1685083
Median Absolute Deviation (MAD)79
Skewness1.247429186
Sum547893
Variance17145.12081
MonotonicityNot monotonic
2021-08-09T10:52:44.847416image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
620
 
0.6%
119
 
0.6%
12919
 
0.6%
519
 
0.6%
9719
 
0.6%
8119
 
0.6%
919
 
0.6%
9818
 
0.5%
1618
 
0.5%
8518
 
0.5%
Other values (510)3070
93.4%
(Missing)29
 
0.9%
ValueCountFrequency (%)
119
0.6%
211
0.3%
314
0.4%
415
0.5%
519
0.6%
620
0.6%
79
0.3%
818
0.5%
919
0.6%
1017
0.5%
ValueCountFrequency (%)
8131
< 0.1%
7651
< 0.1%
7502
0.1%
7391
< 0.1%
7381
< 0.1%
7331
< 0.1%
7231
< 0.1%
7121
< 0.1%
7031
< 0.1%
6821
< 0.1%

duration
Real number (ℝ≥0)

HIGH CORRELATION

Distinct136
Distinct (%)4.1%
Missing5
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean106.4594759
Minimum7
Maximum280
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size51.4 KiB
2021-08-09T10:52:44.988011image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile84
Q193
median103
Q3116
95-th percentile141
Maximum280
Range273
Interquartile range (IQR)23

Descriptive statistics

Standard deviation19.36236771
Coefficient of variation (CV)0.1818754746
Kurtosis5.858641477
Mean106.4594759
Median Absolute Deviation (MAD)11
Skewness1.460797069
Sum349400
Variance374.9012832
MonotonicityNot monotonic
2021-08-09T10:52:45.097355image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
90131
 
4.0%
10199
 
3.0%
9795
 
2.9%
9594
 
2.9%
9894
 
2.9%
10092
 
2.8%
9490
 
2.7%
9389
 
2.7%
10281
 
2.5%
10681
 
2.5%
Other values (126)2336
71.1%
ValueCountFrequency (%)
71
< 0.1%
201
< 0.1%
351
< 0.1%
411
< 0.1%
461
< 0.1%
471
< 0.1%
521
< 0.1%
531
< 0.1%
601
< 0.1%
631
< 0.1%
ValueCountFrequency (%)
2801
 
< 0.1%
2401
 
< 0.1%
2201
 
< 0.1%
2161
 
< 0.1%
2151
 
< 0.1%
2061
 
< 0.1%
2013
0.1%
1961
 
< 0.1%
1951
 
< 0.1%
1941
 
< 0.1%

director_facebook_likes
Real number (ℝ≥0)

ZEROS

Distinct392
Distinct (%)11.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean603.7158503
Minimum0
Maximum23000
Zeros548
Zeros (%)16.7%
Negative0
Negative (%)0.0%
Memory size51.4 KiB
2021-08-09T10:52:45.237949image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q18
median50
Q3189
95-th percentile905
Maximum23000
Range23000
Interquartile range (IQR)181

Descriptive statistics

Standard deviation2654.118673
Coefficient of variation (CV)4.396304439
Kurtosis35.38076684
Mean603.7158503
Median Absolute Deviation (MAD)50
Skewness5.907418613
Sum1984414
Variance7044345.93
MonotonicityNot monotonic
2021-08-09T10:52:45.347296image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0548
 
16.7%
353
 
1.6%
244
 
1.3%
743
 
1.3%
643
 
1.3%
1139
 
1.2%
439
 
1.2%
838
 
1.2%
937
 
1.1%
1035
 
1.1%
Other values (382)2368
72.0%
ValueCountFrequency (%)
0548
16.7%
244
 
1.3%
353
 
1.6%
439
 
1.2%
531
 
0.9%
643
 
1.3%
743
 
1.3%
838
 
1.2%
937
 
1.1%
1035
 
1.1%
ValueCountFrequency (%)
230001
 
< 0.1%
220008
0.2%
210007
 
0.2%
200001
 
< 0.1%
180003
 
0.1%
170008
0.2%
1600018
0.5%
150001
 
< 0.1%
1400014
0.4%
1300012
0.4%

actor_3_facebook_likes
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct836
Distinct (%)25.5%
Missing10
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean744.5691181
Minimum0
Maximum23000
Zeros51
Zeros (%)1.6%
Negative0
Negative (%)0.0%
Memory size51.4 KiB
2021-08-09T10:52:45.472270image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile16.8
Q1172
median416
Q3681
95-th percentile1000
Maximum23000
Range23000
Interquartile range (IQR)509

Descriptive statistics

Standard deviation1876.909299
Coefficient of variation (CV)2.520799282
Kurtosis48.55939805
Mean744.5691181
Median Absolute Deviation (MAD)251
Skewness6.549472711
Sum2439953
Variance3522788.515
MonotonicityNot monotonic
2021-08-09T10:52:45.597238image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1000106
 
3.2%
051
 
1.6%
1100024
 
0.7%
200023
 
0.7%
300020
 
0.6%
82615
 
0.5%
32215
 
0.5%
1000014
 
0.4%
314
 
0.4%
44213
 
0.4%
Other values (826)2982
90.7%
ValueCountFrequency (%)
051
1.6%
27
 
0.2%
314
 
0.4%
46
 
0.2%
59
 
0.3%
67
 
0.2%
712
 
0.4%
810
 
0.3%
96
 
0.2%
105
 
0.2%
ValueCountFrequency (%)
230002
 
0.1%
200001
 
< 0.1%
190004
 
0.1%
170001
 
< 0.1%
160003
 
0.1%
150001
 
< 0.1%
140006
 
0.2%
130002
 
0.1%
120006
 
0.2%
1100024
0.7%

actor_2_name
Categorical

HIGH CARDINALITY
UNIFORM

Distinct2074
Distinct (%)63.2%
Missing7
Missing (%)0.2%
Memory size251.6 KiB
Charlize Theron
 
13
Morgan Freeman
 
12
James Franco
 
10
Judy Greer
 
9
Adam Sandler
 
9
Other values (2069)
3227 

Length

Max length28
Median length13
Mean length13.11859756
Min length3

Characters and Unicode

Total characters43029
Distinct characters69
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1449 ?
Unique (%)44.2%

Sample

1st rowJohn Gallagher Jr.
2nd rowKelly LeBrock
3rd rowHeath Ledger
4th rowEric Idle
5th rowBrad Renfro

Common Values

ValueCountFrequency (%)
Charlize Theron13
 
0.4%
Morgan Freeman12
 
0.4%
James Franco10
 
0.3%
Judy Greer9
 
0.3%
Adam Sandler9
 
0.3%
Thomas Kretschmann9
 
0.3%
Rosario Dawson8
 
0.2%
Meryl Streep8
 
0.2%
Angelina Jolie Pitt8
 
0.2%
Will Ferrell8
 
0.2%
Other values (2064)3186
96.9%

Length

2021-08-09T10:52:45.909667image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
michael64
 
0.9%
david39
 
0.6%
scott38
 
0.6%
james36
 
0.5%
tom31
 
0.5%
kevin31
 
0.5%
thomas31
 
0.5%
john30
 
0.4%
adam30
 
0.4%
jason28
 
0.4%
Other values (2786)6437
94.7%

Most occurring characters

ValueCountFrequency (%)
e4090
 
9.5%
a3891
 
9.0%
3515
 
8.2%
n3106
 
7.2%
r2863
 
6.7%
i2619
 
6.1%
o2375
 
5.5%
l2224
 
5.2%
t1522
 
3.5%
s1469
 
3.4%
Other values (59)15355
35.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter32360
75.2%
Uppercase Letter6983
 
16.2%
Space Separator3515
 
8.2%
Other Punctuation125
 
0.3%
Dash Punctuation40
 
0.1%
Decimal Number6
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e4090
12.6%
a3891
12.0%
n3106
9.6%
r2863
8.8%
i2619
 
8.1%
o2375
 
7.3%
l2224
 
6.9%
t1522
 
4.7%
s1469
 
4.5%
h1188
 
3.7%
Other values (27)7013
21.7%
Uppercase Letter
ValueCountFrequency (%)
M658
 
9.4%
S575
 
8.2%
C566
 
8.1%
J515
 
7.4%
B477
 
6.8%
D438
 
6.3%
A412
 
5.9%
R390
 
5.6%
L329
 
4.7%
T302
 
4.3%
Other values (16)2321
33.2%
Other Punctuation
ValueCountFrequency (%)
.85
68.0%
'40
32.0%
Decimal Number
ValueCountFrequency (%)
53
50.0%
03
50.0%
Space Separator
ValueCountFrequency (%)
3515
100.0%
Dash Punctuation
ValueCountFrequency (%)
-40
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin39343
91.4%
Common3686
 
8.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
e4090
 
10.4%
a3891
 
9.9%
n3106
 
7.9%
r2863
 
7.3%
i2619
 
6.7%
o2375
 
6.0%
l2224
 
5.7%
t1522
 
3.9%
s1469
 
3.7%
h1188
 
3.0%
Other values (53)13996
35.6%
Common
ValueCountFrequency (%)
3515
95.4%
.85
 
2.3%
-40
 
1.1%
'40
 
1.1%
53
 
0.1%
03
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII42979
99.9%
Latin 1 Sup50
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e4090
 
9.5%
a3891
 
9.1%
3515
 
8.2%
n3106
 
7.2%
r2863
 
6.7%
i2619
 
6.1%
o2375
 
5.5%
l2224
 
5.2%
t1522
 
3.5%
s1469
 
3.4%
Other values (48)15305
35.6%
Latin 1 Sup
ValueCountFrequency (%)
é14
28.0%
í13
26.0%
ë6
12.0%
á5
 
10.0%
å3
 
6.0%
ø2
 
4.0%
ó2
 
4.0%
ï2
 
4.0%
à1
 
2.0%
è1
 
2.0%

actor_1_facebook_likes
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct724
Distinct (%)22.1%
Missing4
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean7728.218093
Minimum0
Maximum640000
Zeros19
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size51.4 KiB
2021-08-09T10:52:46.019014image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile141
Q1694.5
median1000
Q312000
95-th percentile26000
Maximum640000
Range640000
Interquartile range (IQR)11305.5

Descriptive statistics

Standard deviation17602.61506
Coefficient of variation (CV)2.27770682
Kurtosis550.8209414
Mean7728.218093
Median Absolute Deviation (MAD)907
Skewness17.94668335
Sum25371740
Variance309852056.9
MonotonicityNot monotonic
2021-08-09T10:52:46.143987image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1000276
 
8.4%
11000153
 
4.7%
2000142
 
4.3%
3000121
 
3.7%
1400097
 
3.0%
1200092
 
2.8%
1300084
 
2.6%
1000076
 
2.3%
1800073
 
2.2%
1500058
 
1.8%
Other values (714)2111
64.2%
ValueCountFrequency (%)
019
0.6%
23
 
0.1%
32
 
0.1%
52
 
0.1%
61
 
< 0.1%
81
 
< 0.1%
101
 
< 0.1%
111
 
< 0.1%
123
 
0.1%
141
 
< 0.1%
ValueCountFrequency (%)
6400001
 
< 0.1%
2600003
 
0.1%
1640002
 
0.1%
1370002
 
0.1%
870008
0.2%
770001
 
< 0.1%
4900017
0.5%
460001
 
< 0.1%
450002
 
0.1%
440002
 
0.1%

gross
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct2836
Distinct (%)98.0%
Missing393
Missing (%)12.0%
Infinite0
Infinite (%)0.0%
Mean50818577.63
Minimum162
Maximum760505847
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size51.4 KiB
2021-08-09T10:52:46.268958image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum162
5-th percentile96772.35
Q16009417.25
median26538990
Q364695209.5
95-th percentile191073622.2
Maximum760505847
Range760505685
Interquartile range (IQR)58685792.25

Descriptive statistics

Standard deviation71357422.73
Coefficient of variation (CV)1.404160173
Kurtosis13.02186246
Mean50818577.63
Median Absolute Deviation (MAD)24031016.5
Skewness2.979682183
Sum1.470689637 × 1011
Variance5.091881778 × 1015
MonotonicityNot monotonic
2021-08-09T10:52:46.393926image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1773436753
 
0.1%
2180512603
 
0.1%
793637852
 
0.1%
437712912
 
0.1%
340143982
 
0.1%
1445123102
 
0.1%
508152882
 
0.1%
586070072
 
0.1%
285016512
 
0.1%
439828422
 
0.1%
Other values (2826)2872
87.4%
(Missing)393
 
12.0%
ValueCountFrequency (%)
1621
< 0.1%
7031
< 0.1%
7211
< 0.1%
7281
< 0.1%
8281
< 0.1%
13321
< 0.1%
15211
< 0.1%
17111
< 0.1%
24361
< 0.1%
24681
< 0.1%
ValueCountFrequency (%)
7605058471
< 0.1%
6521772711
< 0.1%
6232795471
< 0.1%
5333160611
< 0.1%
4745446771
< 0.1%
4589915991
< 0.1%
4481306421
< 0.1%
4364710361
< 0.1%
4246455771
< 0.1%
4230326281
< 0.1%

genres
Categorical

HIGH CARDINALITY

Distinct688
Distinct (%)20.9%
Missing0
Missing (%)0.0%
Memory size273.8 KiB
Drama
 
158
Comedy
 
150
Comedy|Drama|Romance
 
129
Comedy|Drama
 
128
Comedy|Romance
 
124
Other values (683)
2598 

Length

Max length60
Median length20
Mean length20.28262854
Min length5

Characters and Unicode

Total characters66669
Distinct characters33
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique381 ?
Unique (%)11.6%

Sample

1st rowDrama|Horror|Mystery|Sci-Fi|Thriller
2nd rowDrama
3rd rowComedy|Drama|Romance
4th rowAdventure|Comedy|Family
5th rowCrime|Drama|Thriller

Common Values

ValueCountFrequency (%)
Drama158
 
4.8%
Comedy150
 
4.6%
Comedy|Drama|Romance129
 
3.9%
Comedy|Drama128
 
3.9%
Comedy|Romance124
 
3.8%
Drama|Romance105
 
3.2%
Crime|Drama|Thriller76
 
2.3%
Horror51
 
1.6%
Drama|Thriller51
 
1.6%
Action|Crime|Drama|Thriller48
 
1.5%
Other values (678)2267
69.0%

Length

2021-08-09T10:52:46.721976image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
drama158
 
4.8%
comedy150
 
4.6%
comedy|drama|romance129
 
3.9%
comedy|drama128
 
3.9%
comedy|romance124
 
3.8%
drama|romance105
 
3.2%
crime|drama|thriller76
 
2.3%
drama|thriller51
 
1.6%
horror51
 
1.6%
action|crime|drama|thriller48
 
1.5%
Other values (678)2267
69.0%

Most occurring characters

ValueCountFrequency (%)
r6838
 
10.3%
|6121
 
9.2%
a5846
 
8.8%
e5215
 
7.8%
m4876
 
7.3%
i4257
 
6.4%
o4149
 
6.2%
y3111
 
4.7%
n2902
 
4.4%
t2583
 
3.9%
Other values (23)20771
31.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter50366
75.5%
Uppercase Letter9795
 
14.7%
Math Symbol6121
 
9.2%
Dash Punctuation387
 
0.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r6838
13.6%
a5846
11.6%
e5215
10.4%
m4876
9.7%
i4257
8.5%
o4149
8.2%
y3111
 
6.2%
n2902
 
5.8%
t2583
 
5.1%
l2356
 
4.7%
Other values (9)8233
16.3%
Uppercase Letter
ValueCountFrequency (%)
C1838
18.8%
D1759
18.0%
A1473
15.0%
F1151
11.8%
T956
9.8%
R730
 
7.5%
M544
 
5.6%
S522
 
5.3%
H471
 
4.8%
B200
 
2.0%
Other values (2)151
 
1.5%
Math Symbol
ValueCountFrequency (%)
|6121
100.0%
Dash Punctuation
ValueCountFrequency (%)
-387
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin60161
90.2%
Common6508
 
9.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
r6838
 
11.4%
a5846
 
9.7%
e5215
 
8.7%
m4876
 
8.1%
i4257
 
7.1%
o4149
 
6.9%
y3111
 
5.2%
n2902
 
4.8%
t2583
 
4.3%
l2356
 
3.9%
Other values (21)18028
30.0%
Common
ValueCountFrequency (%)
|6121
94.1%
-387
 
5.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII66669
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r6838
 
10.3%
|6121
 
9.2%
a5846
 
8.8%
e5215
 
7.8%
m4876
 
7.3%
i4257
 
6.4%
o4149
 
6.2%
y3111
 
4.7%
n2902
 
4.4%
t2583
 
3.9%
Other values (23)20771
31.2%

actor_1_name
Categorical

HIGH CARDINALITY

Distinct1430
Distinct (%)43.6%
Missing4
Missing (%)0.1%
Memory size251.9 KiB
Johnny Depp
 
30
J.K. Simmons
 
29
Robert De Niro
 
28
Matt Damon
 
26
Nicolas Cage
 
23
Other values (1425)
3147 

Length

Max length27
Median length13
Mean length13.17605848
Min length4

Characters and Unicode

Total characters43257
Distinct characters71
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique936 ?
Unique (%)28.5%

Sample

1st rowBradley Cooper
2nd rowChristopher Lambert
3rd rowJoseph Gordon-Levitt
4th rowIoan Gruffudd
5th rowBrian Dennehy

Common Values

ValueCountFrequency (%)
Johnny Depp30
 
0.9%
J.K. Simmons29
 
0.9%
Robert De Niro28
 
0.9%
Matt Damon26
 
0.8%
Nicolas Cage23
 
0.7%
Robert Downey Jr.23
 
0.7%
Liam Neeson23
 
0.7%
Jason Statham23
 
0.7%
Gerard Butler21
 
0.6%
Scarlett Johansson21
 
0.6%
Other values (1420)3036
92.4%

Length

2021-08-09T10:52:47.039622image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
robert69
 
1.0%
michael61
 
0.9%
tom57
 
0.8%
jason44
 
0.6%
jennifer41
 
0.6%
matt40
 
0.6%
jr38
 
0.6%
james37
 
0.5%
will37
 
0.5%
ryan36
 
0.5%
Other values (2033)6358
93.3%

Most occurring characters

ValueCountFrequency (%)
e4022
 
9.3%
a3778
 
8.7%
3535
 
8.2%
n3139
 
7.3%
r2758
 
6.4%
i2739
 
6.3%
o2489
 
5.8%
l2216
 
5.1%
t1655
 
3.8%
s1551
 
3.6%
Other values (61)15375
35.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter32513
75.2%
Uppercase Letter6997
 
16.2%
Space Separator3535
 
8.2%
Other Punctuation168
 
0.4%
Dash Punctuation42
 
0.1%
Decimal Number2
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e4022
12.4%
a3778
11.6%
n3139
9.7%
r2758
 
8.5%
i2739
 
8.4%
o2489
 
7.7%
l2216
 
6.8%
t1655
 
5.1%
s1551
 
4.8%
h1179
 
3.6%
Other values (27)6987
21.5%
Uppercase Letter
ValueCountFrequency (%)
J678
 
9.7%
M579
 
8.3%
C555
 
7.9%
S552
 
7.9%
D477
 
6.8%
B469
 
6.7%
R413
 
5.9%
H334
 
4.8%
A325
 
4.6%
W312
 
4.5%
Other values (18)2303
32.9%
Other Punctuation
ValueCountFrequency (%)
.140
83.3%
'28
 
16.7%
Decimal Number
ValueCountFrequency (%)
51
50.0%
01
50.0%
Space Separator
ValueCountFrequency (%)
3535
100.0%
Dash Punctuation
ValueCountFrequency (%)
-42
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin39510
91.3%
Common3747
 
8.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
e4022
 
10.2%
a3778
 
9.6%
n3139
 
7.9%
r2758
 
7.0%
i2739
 
6.9%
o2489
 
6.3%
l2216
 
5.6%
t1655
 
4.2%
s1551
 
3.9%
h1179
 
3.0%
Other values (55)13984
35.4%
Common
ValueCountFrequency (%)
3535
94.3%
.140
 
3.7%
-42
 
1.1%
'28
 
0.7%
51
 
< 0.1%
01
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII43210
99.9%
Latin 1 Sup47
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e4022
 
9.3%
a3778
 
8.7%
3535
 
8.2%
n3139
 
7.3%
r2758
 
6.4%
i2739
 
6.3%
o2489
 
5.8%
l2216
 
5.1%
t1655
 
3.8%
s1551
 
3.6%
Other values (48)15328
35.5%
Latin 1 Sup
ValueCountFrequency (%)
ë15
31.9%
é12
25.5%
á4
 
8.5%
å4
 
8.5%
à2
 
4.3%
ø2
 
4.3%
ñ2
 
4.3%
Á1
 
2.1%
í1
 
2.1%
ô1
 
2.1%
Other values (3)3
 
6.4%

movie_title
Categorical

HIGH CARDINALITY
UNIFORM

Distinct3223
Distinct (%)98.1%
Missing0
Missing (%)0.0%
Memory size256.9 KiB
Ben-Hur
 
3
Home
 
3
King Kong
 
3
Oz the Great and Powerful
 
2
Goosebumps
 
2
Other values (3218)
3274 

Length

Max length83
Median length13
Mean length14.97322787
Min length1

Characters and Unicode

Total characters49217
Distinct characters78
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3162 ?
Unique (%)96.2%

Sample

1st row10 Cloverfield Lane
2nd row10 Days in a Madhouse
3rd row10 Things I Hate About You
4th row102 Dalmatians
5th row10th & Wolf

Common Values

ValueCountFrequency (%)
Ben-Hur3
 
0.1%
Home3
 
0.1%
King Kong3
 
0.1%
Oz the Great and Powerful2
 
0.1%
Goosebumps2
 
0.1%
The Gambler2
 
0.1%
RoboCop2
 
0.1%
The Fast and the Furious2
 
0.1%
The Island2
 
0.1%
The Last House on the Left2
 
0.1%
Other values (3213)3264
99.3%

Length

2021-08-09T10:52:47.305183image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
the1030
 
11.6%
of315
 
3.5%
a110
 
1.2%
and83
 
0.9%
in77
 
0.9%
269
 
0.8%
to69
 
0.8%
man41
 
0.5%
40
 
0.4%
movie37
 
0.4%
Other values (3588)7021
79.0%

Most occurring characters

ValueCountFrequency (%)
5605
 
11.4%
e5075
 
10.3%
a3011
 
6.1%
o3002
 
6.1%
n2686
 
5.5%
r2600
 
5.3%
i2543
 
5.2%
t2469
 
5.0%
s1944
 
3.9%
h1918
 
3.9%
Other values (68)18364
37.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter34829
70.8%
Uppercase Letter7825
 
15.9%
Space Separator5605
 
11.4%
Other Punctuation569
 
1.2%
Decimal Number326
 
0.7%
Dash Punctuation62
 
0.1%
Other Symbol1
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e5075
14.6%
a3011
 
8.6%
o3002
 
8.6%
n2686
 
7.7%
r2600
 
7.5%
i2543
 
7.3%
t2469
 
7.1%
s1944
 
5.6%
h1918
 
5.5%
l1596
 
4.6%
Other values (19)7985
22.9%
Uppercase Letter
ValueCountFrequency (%)
T1102
14.1%
S680
 
8.7%
M549
 
7.0%
B500
 
6.4%
D458
 
5.9%
C438
 
5.6%
A427
 
5.5%
L373
 
4.8%
H358
 
4.6%
W331
 
4.2%
Other values (17)2609
33.3%
Decimal Number
ValueCountFrequency (%)
296
29.4%
351
15.6%
150
15.3%
049
15.0%
418
 
5.5%
817
 
5.2%
514
 
4.3%
912
 
3.7%
610
 
3.1%
79
 
2.8%
Other Punctuation
ValueCountFrequency (%)
:237
41.7%
'139
24.4%
.88
 
15.5%
,40
 
7.0%
&32
 
5.6%
!15
 
2.6%
?12
 
2.1%
/5
 
0.9%
·1
 
0.2%
Space Separator
ValueCountFrequency (%)
5605
100.0%
Other Symbol
ValueCountFrequency (%)
°1
100.0%
Dash Punctuation
ValueCountFrequency (%)
-62
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin42654
86.7%
Common6563
 
13.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
e5075
 
11.9%
a3011
 
7.1%
o3002
 
7.0%
n2686
 
6.3%
r2600
 
6.1%
i2543
 
6.0%
t2469
 
5.8%
s1944
 
4.6%
h1918
 
4.5%
l1596
 
3.7%
Other values (46)15810
37.1%
Common
ValueCountFrequency (%)
5605
85.4%
:237
 
3.6%
'139
 
2.1%
296
 
1.5%
.88
 
1.3%
-62
 
0.9%
351
 
0.8%
150
 
0.8%
049
 
0.7%
,40
 
0.6%
Other values (12)146
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII49211
> 99.9%
Latin 1 Sup6
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5605
 
11.4%
e5075
 
10.3%
a3011
 
6.1%
o3002
 
6.1%
n2686
 
5.5%
r2600
 
5.3%
i2543
 
5.2%
t2469
 
5.0%
s1944
 
4.0%
h1918
 
3.9%
Other values (62)18358
37.3%
Latin 1 Sup
ValueCountFrequency (%)
Æ1
16.7%
°1
16.7%
ü1
16.7%
é1
16.7%
è1
16.7%
·1
16.7%

num_voted_users
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3211
Distinct (%)97.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean89896.06784
Minimum6
Maximum1676169
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size51.4 KiB
2021-08-09T10:52:47.430156image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile515.8
Q110152
median39788
Q3108159
95-th percentile351101.2
Maximum1676169
Range1676163
Interquartile range (IQR)98007

Descriptive statistics

Standard deviation139429.4095
Coefficient of variation (CV)1.551006766
Kurtosis21.7165376
Mean89896.06784
Median Absolute Deviation (MAD)35401
Skewness3.734628914
Sum295488375
Variance1.944056022 × 1010
MonotonicityNot monotonic
2021-08-09T10:52:47.570748image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
573
 
0.1%
623
 
0.1%
1623
 
0.1%
383
 
0.1%
63
 
0.1%
533
 
0.1%
1182
 
0.1%
1432
 
0.1%
43772
 
0.1%
195472
 
0.1%
Other values (3201)3261
99.2%
ValueCountFrequency (%)
63
0.1%
71
 
< 0.1%
82
0.1%
131
 
< 0.1%
152
0.1%
222
0.1%
231
 
< 0.1%
241
 
< 0.1%
251
 
< 0.1%
271
 
< 0.1%
ValueCountFrequency (%)
16761691
< 0.1%
14682001
< 0.1%
13474611
< 0.1%
12387461
< 0.1%
12177521
< 0.1%
12157181
< 0.1%
11443371
< 0.1%
11004461
< 0.1%
9954151
< 0.1%
9826371
< 0.1%

cast_total_facebook_likes
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2861
Distinct (%)87.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11334.00791
Minimum0
Maximum656730
Zeros23
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size51.4 KiB
2021-08-09T10:52:47.711340image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile278
Q11738.5
median3769
Q315773.5
95-th percentile40014.2
Maximum656730
Range656730
Interquartile range (IQR)14035

Descriptive statistics

Standard deviation20947.7558
Coefficient of variation (CV)1.848221385
Kurtosis308.8798109
Mean11334.00791
Median Absolute Deviation (MAD)2927
Skewness12.47454243
Sum37254884
Variance438808472.9
MonotonicityNot monotonic
2021-08-09T10:52:47.836278image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
023
 
0.7%
20204
 
0.1%
16513
 
0.1%
15543
 
0.1%
24863
 
0.1%
2103
 
0.1%
22593
 
0.1%
18253
 
0.1%
27053
 
0.1%
22513
 
0.1%
Other values (2851)3236
98.4%
ValueCountFrequency (%)
023
0.7%
23
 
0.1%
31
 
< 0.1%
52
 
0.1%
61
 
< 0.1%
101
 
< 0.1%
121
 
< 0.1%
153
 
0.1%
191
 
< 0.1%
211
 
< 0.1%
ValueCountFrequency (%)
6567301
< 0.1%
3037171
< 0.1%
2839391
< 0.1%
2635841
< 0.1%
2618181
< 0.1%
1701181
< 0.1%
1402681
< 0.1%
1377121
< 0.1%
1207971
< 0.1%
1080161
< 0.1%

actor_3_name
Categorical

HIGH CARDINALITY
UNIFORM

Distinct2404
Distinct (%)73.4%
Missing10
Missing (%)0.3%
Memory size251.7 KiB
Steve Coogan
 
8
Ben Mendelsohn
 
7
Stephen Root
 
7
Sam Shepard
 
6
Jose Pablo Cantillo
 
6
Other values (2399)
3243 

Length

Max length29
Median length13
Mean length13.06591395
Min length3

Characters and Unicode

Total characters42817
Distinct characters78
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1843 ?
Unique (%)56.2%

Sample

1st rowSumalee Montano
2nd rowAlexandra Callas
3rd rowAndrew Keegan
4th rowJim Carter
5th rowDash Mihok

Common Values

ValueCountFrequency (%)
Steve Coogan8
 
0.2%
Ben Mendelsohn7
 
0.2%
Stephen Root7
 
0.2%
Sam Shepard6
 
0.2%
Jose Pablo Cantillo6
 
0.2%
Thomas Lennon6
 
0.2%
Anne Hathaway6
 
0.2%
Steve Carell6
 
0.2%
Mike Epps6
 
0.2%
Derek Jacobi5
 
0.2%
Other values (2394)3214
97.8%
(Missing)10
 
0.3%

Length

2021-08-09T10:52:48.133117image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
michael65
 
1.0%
james49
 
0.7%
david45
 
0.7%
john38
 
0.6%
tom30
 
0.4%
steve29
 
0.4%
robert26
 
0.4%
chris24
 
0.4%
smith24
 
0.4%
peter24
 
0.4%
Other values (3140)6423
94.8%

Most occurring characters

ValueCountFrequency (%)
e4051
 
9.5%
a3968
 
9.3%
3500
 
8.2%
n3003
 
7.0%
r2721
 
6.4%
i2629
 
6.1%
o2263
 
5.3%
l2231
 
5.2%
t1531
 
3.6%
s1517
 
3.5%
Other values (68)15403
36.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter32136
75.1%
Uppercase Letter6986
 
16.3%
Space Separator3500
 
8.2%
Other Punctuation144
 
0.3%
Dash Punctuation49
 
0.1%
Decimal Number2
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e4051
12.6%
a3968
12.3%
n3003
9.3%
r2721
 
8.5%
i2629
 
8.2%
o2263
 
7.0%
l2231
 
6.9%
t1531
 
4.8%
s1517
 
4.7%
h1226
 
3.8%
Other values (33)6996
21.8%
Uppercase Letter
ValueCountFrequency (%)
M664
 
9.5%
S555
 
7.9%
J553
 
7.9%
B527
 
7.5%
C519
 
7.4%
D436
 
6.2%
A417
 
6.0%
R403
 
5.8%
L347
 
5.0%
T294
 
4.2%
Other values (19)2271
32.5%
Other Punctuation
ValueCountFrequency (%)
.105
72.9%
'39
 
27.1%
Decimal Number
ValueCountFrequency (%)
51
50.0%
01
50.0%
Space Separator
ValueCountFrequency (%)
3500
100.0%
Dash Punctuation
ValueCountFrequency (%)
-49
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin39122
91.4%
Common3695
 
8.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
e4051
 
10.4%
a3968
 
10.1%
n3003
 
7.7%
r2721
 
7.0%
i2629
 
6.7%
o2263
 
5.8%
l2231
 
5.7%
t1531
 
3.9%
s1517
 
3.9%
h1226
 
3.1%
Other values (62)13982
35.7%
Common
ValueCountFrequency (%)
3500
94.7%
.105
 
2.8%
-49
 
1.3%
'39
 
1.1%
51
 
< 0.1%
01
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII42747
99.8%
Latin 1 Sup70
 
0.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e4051
 
9.5%
a3968
 
9.3%
3500
 
8.2%
n3003
 
7.0%
r2721
 
6.4%
i2629
 
6.2%
o2263
 
5.3%
l2231
 
5.2%
t1531
 
3.6%
s1517
 
3.5%
Other values (48)15333
35.9%
Latin 1 Sup
ValueCountFrequency (%)
é23
32.9%
ë6
 
8.6%
í6
 
8.6%
ó5
 
7.1%
à5
 
7.1%
á5
 
7.1%
ü4
 
5.7%
å2
 
2.9%
ø2
 
2.9%
Ó2
 
2.9%
Other values (10)10
14.3%

facenumber_in_poster
Real number (ℝ≥0)

ZEROS

Distinct17
Distinct (%)0.5%
Missing12
Missing (%)0.4%
Infinite0
Infinite (%)0.0%
Mean1.423206107
Minimum0
Maximum31
Zeros1386
Zeros (%)42.2%
Negative0
Negative (%)0.0%
Memory size51.4 KiB
2021-08-09T10:52:48.242466image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile5
Maximum31
Range31
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.015019682
Coefficient of variation (CV)1.41583125
Kurtosis21.47244929
Mean1.423206107
Median Absolute Deviation (MAD)1
Skewness3.12564875
Sum4661
Variance4.060304318
MonotonicityNot monotonic
2021-08-09T10:52:48.336195image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
01386
42.2%
1800
24.3%
2447
 
13.6%
3267
 
8.1%
4144
 
4.4%
578
 
2.4%
654
 
1.6%
738
 
1.2%
827
 
0.8%
912
 
0.4%
Other values (7)22
 
0.7%
(Missing)12
 
0.4%
ValueCountFrequency (%)
01386
42.2%
1800
24.3%
2447
 
13.6%
3267
 
8.1%
4144
 
4.4%
578
 
2.4%
654
 
1.6%
738
 
1.2%
827
 
0.8%
912
 
0.4%
ValueCountFrequency (%)
311
 
< 0.1%
191
 
< 0.1%
154
 
0.1%
132
 
0.1%
123
 
0.1%
114
 
0.1%
107
 
0.2%
912
 
0.4%
827
0.8%
738
1.2%

plot_keywords
Categorical

HIGH CARDINALITY
MISSING
UNIFORM

Distinct3119
Distinct (%)97.9%
Missing102
Missing (%)3.1%
Memory size370.0 KiB
based on novel
 
4
alien friendship|alien invasion|australia|flying car|mother daughter relationship
 
3
animal name in title|ape abducts a woman|gorilla|island|king kong
 
3
coma|mysterious villain|police chase|shooting range|sniper
 
2
astronaut|electrocuted in bathtub|mission|pilot|sister
 
2
Other values (3114)
3171 

Length

Max length138
Median length50
Mean length52.66813187
Min length2

Characters and Unicode

Total characters167748
Distinct characters42
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3057 ?
Unique (%)96.0%

Sample

1st rowalien|bunker|car crash|kidnapping|minimal cast
2nd rowdating|protective father|school|shrew|teen movie
3rd rowdog|parole|parole officer|prison|puppy
4th rowdesert storm|fbi|fbi agent|fragmentation grenade|woman kills attacker
5th rowconvenience store|multiple perspectives|murder|paramedic|van

Common Values

ValueCountFrequency (%)
based on novel4
 
0.1%
alien friendship|alien invasion|australia|flying car|mother daughter relationship3
 
0.1%
animal name in title|ape abducts a woman|gorilla|island|king kong3
 
0.1%
coma|mysterious villain|police chase|shooting range|sniper2
 
0.1%
astronaut|electrocuted in bathtub|mission|pilot|sister2
 
0.1%
kissing in an elevator|mythical hero|neo noir|protective male|silent protagonist2
 
0.1%
babysitting|experiment|nightmare|possession|twin2
 
0.1%
disposing of a dead body|high school student|murder|sociopath|teenage boy2
 
0.1%
alien|earth|giant robot|military|scientist2
 
0.1%
clinical trial|female protagonist|neuropharmacology|psychiatrist|side effect2
 
0.1%
Other values (3109)3161
96.2%
(Missing)102
 
3.1%

Length

2021-08-09T10:52:48.638188image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
in234
 
2.0%
of150
 
1.3%
on144
 
1.2%
to124
 
1.0%
the123
 
1.0%
a122
 
1.0%
york82
 
0.7%
based79
 
0.7%
female66
 
0.6%
by60
 
0.5%
Other values (7804)10689
90.0%

Most occurring characters

ValueCountFrequency (%)
e16399
 
9.8%
a12730
 
7.6%
|12473
 
7.4%
i12248
 
7.3%
r11824
 
7.0%
t10582
 
6.3%
n10315
 
6.1%
o10155
 
6.1%
8688
 
5.2%
s8628
 
5.1%
Other values (32)53706
32.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter145811
86.9%
Math Symbol12473
 
7.4%
Space Separator8688
 
5.2%
Decimal Number641
 
0.4%
Other Punctuation133
 
0.1%
Open Punctuation1
 
< 0.1%
Close Punctuation1
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e16399
11.2%
a12730
 
8.7%
i12248
 
8.4%
r11824
 
8.1%
t10582
 
7.3%
n10315
 
7.1%
o10155
 
7.0%
s8628
 
5.9%
l7189
 
4.9%
c6170
 
4.2%
Other values (16)39571
27.1%
Decimal Number
ValueCountFrequency (%)
0158
24.6%
1158
24.6%
9118
18.4%
254
 
8.4%
834
 
5.3%
529
 
4.5%
727
 
4.2%
323
 
3.6%
622
 
3.4%
418
 
2.8%
Other Punctuation
ValueCountFrequency (%)
.83
62.4%
'50
37.6%
Math Symbol
ValueCountFrequency (%)
|12473
100.0%
Space Separator
ValueCountFrequency (%)
8688
100.0%
Open Punctuation
ValueCountFrequency (%)
(1
100.0%
Close Punctuation
ValueCountFrequency (%)
)1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin145811
86.9%
Common21937
 
13.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e16399
11.2%
a12730
 
8.7%
i12248
 
8.4%
r11824
 
8.1%
t10582
 
7.3%
n10315
 
7.1%
o10155
 
7.0%
s8628
 
5.9%
l7189
 
4.9%
c6170
 
4.2%
Other values (16)39571
27.1%
Common
ValueCountFrequency (%)
|12473
56.9%
8688
39.6%
0158
 
0.7%
1158
 
0.7%
9118
 
0.5%
.83
 
0.4%
254
 
0.2%
'50
 
0.2%
834
 
0.2%
529
 
0.1%
Other values (6)92
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII167748
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e16399
 
9.8%
a12730
 
7.6%
|12473
 
7.4%
i12248
 
7.3%
r11824
 
7.0%
t10582
 
6.3%
n10315
 
6.1%
o10155
 
6.1%
8688
 
5.2%
s8628
 
5.1%
Other values (32)53706
32.0%
Distinct3225
Distinct (%)98.1%
Missing0
Missing (%)0.0%
Memory size375.6 KiB
http://www.imdb.com/title/tt2224026/?ref_=fn_tt_tt_1
 
3
http://www.imdb.com/title/tt2638144/?ref_=fn_tt_tt_1
 
3
http://www.imdb.com/title/tt0360717/?ref_=fn_tt_tt_1
 
3
http://www.imdb.com/title/tt2039393/?ref_=fn_tt_tt_1
 
2
http://www.imdb.com/title/tt1104001/?ref_=fn_tt_tt_1
 
2
Other values (3220)
3274 
ValueCountFrequency (%)
http://www.imdb.com/title/tt2224026/?ref_=fn_tt_tt_13
 
0.1%
http://www.imdb.com/title/tt2638144/?ref_=fn_tt_tt_13
 
0.1%
http://www.imdb.com/title/tt0360717/?ref_=fn_tt_tt_13
 
0.1%
http://www.imdb.com/title/tt2039393/?ref_=fn_tt_tt_12
 
0.1%
http://www.imdb.com/title/tt1104001/?ref_=fn_tt_tt_12
 
0.1%
http://www.imdb.com/title/tt1742334/?ref_=fn_tt_tt_12
 
0.1%
http://www.imdb.com/title/tt1661199/?ref_=fn_tt_tt_12
 
0.1%
http://www.imdb.com/title/tt0232500/?ref_=fn_tt_tt_12
 
0.1%
http://www.imdb.com/title/tt0397065/?ref_=fn_tt_tt_12
 
0.1%
http://www.imdb.com/title/tt0413300/?ref_=fn_tt_tt_12
 
0.1%
Other values (3215)3264
99.3%
ValueCountFrequency (%)
http3287
100.0%
ValueCountFrequency (%)
www.imdb.com3287
100.0%
ValueCountFrequency (%)
/title/tt2224026/3
 
0.1%
/title/tt0360717/3
 
0.1%
/title/tt2638144/3
 
0.1%
/title/tt1051904/2
 
0.1%
/title/tt1401152/2
 
0.1%
/title/tt3040964/2
 
0.1%
/title/tt1139668/2
 
0.1%
/title/tt0365737/2
 
0.1%
/title/tt0380510/2
 
0.1%
/title/tt1291150/2
 
0.1%
Other values (3215)3264
99.3%
ValueCountFrequency (%)
ref_=fn_tt_tt_13287
100.0%
ValueCountFrequency (%)
3287
100.0%

num_user_for_reviews
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct861
Distinct (%)26.3%
Missing11
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean306.0897436
Minimum1
Maximum5060
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size51.4 KiB
2021-08-09T10:52:48.764413image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile9
Q175
median177
Q3371.25
95-th percentile1017.25
Maximum5060
Range5059
Interquartile range (IQR)296.25

Descriptive statistics

Standard deviation418.2106594
Coefficient of variation (CV)1.366300793
Kurtosis22.24593955
Mean306.0897436
Median Absolute Deviation (MAD)125
Skewness3.847411559
Sum1002750
Variance174900.1556
MonotonicityNot monotonic
2021-08-09T10:52:48.902403image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
135
 
1.1%
327
 
0.8%
822
 
0.7%
1021
 
0.6%
221
 
0.6%
5317
 
0.5%
617
 
0.5%
2017
 
0.5%
2616
 
0.5%
1516
 
0.5%
Other values (851)3067
93.3%
ValueCountFrequency (%)
135
1.1%
221
0.6%
327
0.8%
412
 
0.4%
511
 
0.3%
617
0.5%
77
 
0.2%
822
0.7%
914
 
0.4%
1021
0.6%
ValueCountFrequency (%)
50601
< 0.1%
46671
< 0.1%
36461
< 0.1%
35971
< 0.1%
35161
< 0.1%
34001
< 0.1%
32861
< 0.1%
31891
< 0.1%
30541
< 0.1%
30181
< 0.1%

language
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct29
Distinct (%)0.9%
Missing4
Missing (%)0.1%
Memory size230.9 KiB
English
3196 
French
 
18
Hindi
 
16
Spanish
 
8
German
 
6
Other values (24)
 
39

Length

Max length10
Median length7
Mean length6.980810235
Min length4

Characters and Unicode

Total characters22918
Distinct characters37
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique12 ?
Unique (%)0.4%

Sample

1st rowEnglish
2nd rowEnglish
3rd rowEnglish
4th rowEnglish
5th rowEnglish

Common Values

ValueCountFrequency (%)
English3196
97.2%
French18
 
0.5%
Hindi16
 
0.5%
Spanish8
 
0.2%
German6
 
0.2%
Hebrew4
 
0.1%
Mandarin3
 
0.1%
Russian2
 
0.1%
Aboriginal2
 
0.1%
Korean2
 
0.1%
Other values (19)26
 
0.8%
(Missing)4
 
0.1%

Length

2021-08-09T10:52:49.198850image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
english3196
97.3%
french18
 
0.5%
hindi16
 
0.5%
spanish8
 
0.2%
german6
 
0.2%
hebrew4
 
0.1%
mandarin3
 
0.1%
japanese2
 
0.1%
dari2
 
0.1%
norwegian2
 
0.1%
Other values (19)26
 
0.8%

Most occurring characters

ValueCountFrequency (%)
n3274
14.3%
i3265
14.2%
h3226
14.1%
s3218
14.0%
l3204
14.0%
g3201
14.0%
E3196
13.9%
a52
 
0.2%
e49
 
0.2%
r45
 
0.2%
Other values (27)188
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter19635
85.7%
Uppercase Letter3283
 
14.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n3274
16.7%
i3265
16.6%
h3226
16.4%
s3218
16.4%
l3204
16.3%
g3201
16.3%
a52
 
0.3%
e49
 
0.2%
r45
 
0.2%
c21
 
0.1%
Other values (9)80
 
0.4%
Uppercase Letter
ValueCountFrequency (%)
E3196
97.3%
H20
 
0.6%
F19
 
0.6%
S9
 
0.3%
G6
 
0.2%
A5
 
0.2%
P4
 
0.1%
M4
 
0.1%
N3
 
0.1%
D3
 
0.1%
Other values (8)14
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
Latin22918
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n3274
14.3%
i3265
14.2%
h3226
14.1%
s3218
14.0%
l3204
14.0%
g3201
14.0%
E3196
13.9%
a52
 
0.2%
e49
 
0.2%
r45
 
0.2%
Other values (27)188
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII22918
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n3274
14.3%
i3265
14.2%
h3226
14.1%
s3218
14.0%
l3204
14.0%
g3201
14.0%
E3196
13.9%
a52
 
0.2%
e49
 
0.2%
r45
 
0.2%
Other values (27)188
 
0.8%

country
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct44
Distinct (%)1.3%
Missing1
Missing (%)< 0.1%
Memory size219.5 KiB
USA
2590 
UK
280 
Canada
 
86
France
 
79
Germany
 
74
Other values (39)
 
177

Length

Max length14
Median length3
Mean length3.398052343
Min length2

Characters and Unicode

Total characters11166
Distinct characters43
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique17 ?
Unique (%)0.5%

Sample

1st rowUSA
2nd rowUSA
3rd rowUSA
4th rowUSA
5th rowUSA

Common Values

ValueCountFrequency (%)
USA2590
78.8%
UK280
 
8.5%
Canada86
 
2.6%
France79
 
2.4%
Germany74
 
2.3%
Australia33
 
1.0%
Spain20
 
0.6%
India16
 
0.5%
New Zealand13
 
0.4%
Ireland10
 
0.3%
Other values (34)85
 
2.6%

Length

2021-08-09T10:52:49.480039image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
usa2590
78.0%
uk280
 
8.4%
canada86
 
2.6%
france79
 
2.4%
germany74
 
2.2%
australia33
 
1.0%
spain20
 
0.6%
india16
 
0.5%
new13
 
0.4%
zealand13
 
0.4%
Other values (38)115
 
3.5%

Most occurring characters

ValueCountFrequency (%)
U2870
25.7%
A2631
23.6%
S2624
23.5%
a629
 
5.6%
n345
 
3.1%
K292
 
2.6%
r229
 
2.1%
e227
 
2.0%
d135
 
1.2%
i113
 
1.0%
Other values (33)1071
 
9.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter8779
78.6%
Lowercase Letter2354
 
21.1%
Space Separator33
 
0.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a629
26.7%
n345
14.7%
r229
 
9.7%
e227
 
9.6%
d135
 
5.7%
i113
 
4.8%
c98
 
4.2%
m89
 
3.8%
y87
 
3.7%
l82
 
3.5%
Other values (13)320
13.6%
Uppercase Letter
ValueCountFrequency (%)
U2870
32.7%
A2631
30.0%
S2624
29.9%
K292
 
3.3%
C96
 
1.1%
F80
 
0.9%
G76
 
0.9%
I37
 
0.4%
N18
 
0.2%
Z13
 
0.1%
Other values (9)42
 
0.5%
Space Separator
ValueCountFrequency (%)
33
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin11133
99.7%
Common33
 
0.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
U2870
25.8%
A2631
23.6%
S2624
23.6%
a629
 
5.6%
n345
 
3.1%
K292
 
2.6%
r229
 
2.1%
e227
 
2.0%
d135
 
1.2%
i113
 
1.0%
Other values (32)1038
 
9.3%
Common
ValueCountFrequency (%)
33
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII11166
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
U2870
25.7%
A2631
23.6%
S2624
23.5%
a629
 
5.6%
n345
 
3.1%
K292
 
2.6%
r229
 
2.1%
e227
 
2.0%
d135
 
1.2%
i113
 
1.0%
Other values (33)1071
 
9.6%

content_rating
Categorical

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct10
Distinct (%)0.3%
Missing157
Missing (%)4.8%
Memory size213.5 KiB
R
1386 
PG-13
1157 
PG
427 
Not Rated
 
62
G
 
61
Other values (5)
 
37

Length

Max length9
Median length2
Mean length2.836421725
Min length1

Characters and Unicode

Total characters8878
Distinct characters21
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPG-13
2nd rowR
3rd rowPG-13
4th rowG
5th rowR

Common Values

ValueCountFrequency (%)
R1386
42.2%
PG-131157
35.2%
PG427
 
13.0%
Not Rated62
 
1.9%
G61
 
1.9%
Unrated26
 
0.8%
TV-PG3
 
0.1%
TV-G3
 
0.1%
NC-173
 
0.1%
TV-142
 
0.1%
(Missing)157
 
4.8%

Length

2021-08-09T10:52:49.745597image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-08-09T10:52:49.839330image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
r1386
43.4%
pg-131157
36.2%
pg427
 
13.4%
not62
 
1.9%
rated62
 
1.9%
g61
 
1.9%
unrated26
 
0.8%
nc-173
 
0.1%
tv-g3
 
0.1%
tv-pg3
 
0.1%

Most occurring characters

ValueCountFrequency (%)
G1651
18.6%
P1587
17.9%
R1448
16.3%
-1168
13.2%
11162
13.1%
31157
13.0%
t150
 
1.7%
a88
 
1.0%
e88
 
1.0%
d88
 
1.0%
Other values (11)291
 
3.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter4796
54.0%
Decimal Number2324
26.2%
Dash Punctuation1168
 
13.2%
Lowercase Letter528
 
5.9%
Space Separator62
 
0.7%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
G1651
34.4%
P1587
33.1%
R1448
30.2%
N65
 
1.4%
U26
 
0.5%
T8
 
0.2%
V8
 
0.2%
C3
 
0.1%
Lowercase Letter
ValueCountFrequency (%)
t150
28.4%
a88
16.7%
e88
16.7%
d88
16.7%
o62
11.7%
n26
 
4.9%
r26
 
4.9%
Decimal Number
ValueCountFrequency (%)
11162
50.0%
31157
49.8%
73
 
0.1%
42
 
0.1%
Dash Punctuation
ValueCountFrequency (%)
-1168
100.0%
Space Separator
ValueCountFrequency (%)
62
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin5324
60.0%
Common3554
40.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
G1651
31.0%
P1587
29.8%
R1448
27.2%
t150
 
2.8%
a88
 
1.7%
e88
 
1.7%
d88
 
1.7%
N65
 
1.2%
o62
 
1.2%
U26
 
0.5%
Other values (5)71
 
1.3%
Common
ValueCountFrequency (%)
-1168
32.9%
11162
32.7%
31157
32.6%
62
 
1.7%
73
 
0.1%
42
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII8878
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
G1651
18.6%
P1587
17.9%
R1448
16.3%
-1168
13.2%
11162
13.1%
31157
13.0%
t150
 
1.7%
a88
 
1.0%
e88
 
1.0%
d88
 
1.0%
Other values (11)291
 
3.3%

budget_x
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
SKEWED

Distinct318
Distinct (%)10.5%
Missing265
Missing (%)8.1%
Infinite0
Infinite (%)0.0%
Mean43125533.18
Minimum218
Maximum1.22155 × 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size51.4 KiB
2021-08-09T10:52:50.028037image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum218
5-th percentile552500
Q18000000
median23000000
Q350000000
95-th percentile145000000
Maximum1.22155 × 1010
Range1.221549978 × 1010
Interquartile range (IQR)42000000

Descriptive statistics

Standard deviation226396795.4
Coefficient of variation (CV)5.249715859
Kurtosis2768.629249
Mean43125533.18
Median Absolute Deviation (MAD)17400000
Skewness51.50092352
Sum1.303253613 × 1011
Variance5.125550899 × 1016
MonotonicityNot monotonic
2021-08-09T10:52:50.681951image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20000000121
 
3.7%
30000000108
 
3.3%
1500000099
 
3.0%
4000000097
 
3.0%
2500000091
 
2.8%
1000000089
 
2.7%
3500000089
 
2.7%
6000000074
 
2.3%
500000069
 
2.1%
5000000069
 
2.1%
Other values (308)2116
64.4%
(Missing)265
 
8.1%
ValueCountFrequency (%)
2181
 
< 0.1%
11001
 
< 0.1%
14001
 
< 0.1%
45001
 
< 0.1%
70002
0.1%
90001
 
< 0.1%
100001
 
< 0.1%
130001
 
< 0.1%
150004
0.1%
173501
 
< 0.1%
ValueCountFrequency (%)
1.22155 × 10101
 
< 0.1%
6000000001
 
< 0.1%
3000000001
 
< 0.1%
2637000001
 
< 0.1%
2600000001
 
< 0.1%
2580000002
 
0.1%
2500000007
0.2%
2450000001
 
< 0.1%
2370000001
 
< 0.1%
2300000001
 
< 0.1%

title_year
Real number (ℝ≥0)

Distinct19
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2007.629449
Minimum1980
Maximum2016
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size51.4 KiB
2021-08-09T10:52:50.806547image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1980
5-th percentile2000
Q12004
median2008
Q32012
95-th percentile2015
Maximum2016
Range36
Interquartile range (IQR)8

Descriptive statistics

Standard deviation4.976043317
Coefficient of variation (CV)0.002478566609
Kurtosis-0.8841822536
Mean2007.629449
Median Absolute Deviation (MAD)4
Skewness-0.1387091271
Sum6599078
Variance24.76100709
MonotonicityNot monotonic
2021-08-09T10:52:50.921475image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
2009227
 
6.9%
2014218
 
6.6%
2006209
 
6.4%
2010200
 
6.1%
2011197
 
6.0%
2013196
 
6.0%
2012196
 
6.0%
2008195
 
5.9%
2015194
 
5.9%
2005188
 
5.7%
Other values (9)1267
38.5%
ValueCountFrequency (%)
19801
 
< 0.1%
1999160
4.9%
2000155
4.7%
2001164
5.0%
2002187
5.7%
2003150
4.6%
2004178
5.4%
2005188
5.7%
2006209
6.4%
2007180
5.5%
ValueCountFrequency (%)
201692
2.8%
2015194
5.9%
2014218
6.6%
2013196
6.0%
2012196
6.0%
2011197
6.0%
2010200
6.1%
2009227
6.9%
2008195
5.9%
2007180
5.5%

actor_2_facebook_likes
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct849
Distinct (%)25.9%
Missing7
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean1916.15122
Minimum0
Maximum137000
Zeros34
Zeros (%)1.0%
Negative0
Negative (%)0.0%
Memory size51.4 KiB
2021-08-09T10:52:51.062069image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile44
Q1346
median651
Q3968
95-th percentile11000
Maximum137000
Range137000
Interquartile range (IQR)622

Descriptive statistics

Standard deviation4527.883106
Coefficient of variation (CV)2.363009276
Kurtosis246.1031193
Mean1916.15122
Median Absolute Deviation (MAD)311
Skewness10.08853541
Sum6284976
Variance20501725.42
MonotonicityNot monotonic
2021-08-09T10:52:51.171418image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1000226
 
6.9%
1100081
 
2.5%
200080
 
2.4%
300069
 
2.1%
1000037
 
1.1%
034
 
1.0%
1300029
 
0.9%
1400028
 
0.9%
400027
 
0.8%
82626
 
0.8%
Other values (839)2643
80.4%
ValueCountFrequency (%)
034
1.0%
26
 
0.2%
35
 
0.2%
44
 
0.1%
55
 
0.2%
62
 
0.1%
71
 
< 0.1%
84
 
0.1%
96
 
0.2%
105
 
0.2%
ValueCountFrequency (%)
1370001
 
< 0.1%
270002
 
0.1%
250003
 
0.1%
230006
0.2%
220009
0.3%
210003
 
0.1%
200006
0.2%
190005
 
0.2%
180007
0.2%
1700013
0.4%

imdb_score
Real number (ℝ≥0)

HIGH CORRELATION

Distinct75
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.29756617
Minimum1.6
Maximum9.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size51.4 KiB
2021-08-09T10:52:51.312012image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1.6
5-th percentile4.23
Q15.7
median6.4
Q37.1
95-th percentile7.8
Maximum9.1
Range7.5
Interquartile range (IQR)1.4

Descriptive statistics

Standard deviation1.099479837
Coefficient of variation (CV)0.1745880564
Kurtosis1.077245072
Mean6.29756617
Median Absolute Deviation (MAD)0.7
Skewness-0.7992152456
Sum20700.1
Variance1.208855913
MonotonicityNot monotonic
2021-08-09T10:52:51.436983image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.7154
 
4.7%
6.4140
 
4.3%
6.5139
 
4.2%
6.6130
 
4.0%
6.3126
 
3.8%
7.1123
 
3.7%
7123
 
3.7%
6.1122
 
3.7%
6.2119
 
3.6%
7.3119
 
3.6%
Other values (65)1992
60.6%
ValueCountFrequency (%)
1.61
 
< 0.1%
1.71
 
< 0.1%
1.93
0.1%
22
0.1%
2.13
0.1%
2.23
0.1%
2.32
0.1%
2.42
0.1%
2.52
0.1%
2.61
 
< 0.1%
ValueCountFrequency (%)
9.11
 
< 0.1%
91
 
< 0.1%
8.91
 
< 0.1%
8.83
 
0.1%
8.74
 
0.1%
8.62
 
0.1%
8.511
0.3%
8.47
0.2%
8.314
0.4%
8.212
0.4%

aspect_ratio
Real number (ℝ≥0)

MISSING

Distinct17
Distinct (%)0.6%
Missing213
Missing (%)6.5%
Infinite0
Infinite (%)0.0%
Mean2.161662329
Minimum1.33
Maximum16
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size51.4 KiB
2021-08-09T10:52:51.561954image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1.33
5-th percentile1.85
Q11.85
median2.35
Q32.35
95-th percentile2.35
Maximum16
Range14.67
Interquartile range (IQR)0.5

Descriptive statistics

Standard deviation0.7549083137
Coefficient of variation (CV)0.3492258266
Kurtosis291.5634928
Mean2.161662329
Median Absolute Deviation (MAD)0
Skewness16.00766404
Sum6644.95
Variance0.5698865622
MonotonicityNot monotonic
2021-08-09T10:52:51.655679image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
2.351724
52.4%
1.851183
36.0%
1.7875
 
2.3%
1.6621
 
0.6%
1.3319
 
0.6%
1.3718
 
0.5%
2.3913
 
0.4%
168
 
0.2%
2.43
 
0.1%
23
 
0.1%
Other values (7)7
 
0.2%
(Missing)213
 
6.5%
ValueCountFrequency (%)
1.3319
 
0.6%
1.3718
 
0.5%
1.441
 
< 0.1%
1.51
 
< 0.1%
1.6621
 
0.6%
1.751
 
< 0.1%
1.7875
 
2.3%
1.851183
36.0%
23
 
0.1%
2.21
 
< 0.1%
ValueCountFrequency (%)
168
 
0.2%
2.761
 
< 0.1%
2.551
 
< 0.1%
2.43
 
0.1%
2.3913
 
0.4%
2.351724
52.4%
2.241
 
< 0.1%
2.21
 
< 0.1%
23
 
0.1%
1.851183
36.0%

movie_facebook_likes
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct702
Distinct (%)21.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9646.21296
Minimum0
Maximum349000
Zeros1353
Zeros (%)41.2%
Negative0
Negative (%)0.0%
Memory size51.4 KiB
2021-08-09T10:52:51.796274image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median246
Q311000
95-th percentile52700
Maximum349000
Range349000
Interquartile range (IQR)11000

Descriptive statistics

Standard deviation22508.74244
Coefficient of variation (CV)2.333427899
Kurtosis31.50125416
Mean9646.21296
Median Absolute Deviation (MAD)246
Skewness4.446008683
Sum31707102
Variance506643486.1
MonotonicityNot monotonic
2021-08-09T10:52:51.920931image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01353
41.2%
1000067
 
2.0%
100059
 
1.8%
1100051
 
1.6%
1300045
 
1.4%
1500043
 
1.3%
1200039
 
1.2%
1600035
 
1.1%
1400033
 
1.0%
200029
 
0.9%
Other values (692)1533
46.6%
ValueCountFrequency (%)
01353
41.2%
21
 
< 0.1%
31
 
< 0.1%
41
 
< 0.1%
51
 
< 0.1%
72
 
0.1%
81
 
< 0.1%
92
 
0.1%
101
 
< 0.1%
122
 
0.1%
ValueCountFrequency (%)
3490001
< 0.1%
1990001
< 0.1%
1970001
< 0.1%
1910001
< 0.1%
1900001
< 0.1%
1750001
< 0.1%
1660001
< 0.1%
1650001
< 0.1%
1640001
< 0.1%
1530001
< 0.1%

Horror
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size211.9 KiB
0
2926 
1
361 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3287
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
02926
89.0%
1361
 
11.0%

Length

2021-08-09T10:52:52.159723image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-08-09T10:52:52.237829image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
02926
89.0%
1361
 
11.0%

Most occurring characters

ValueCountFrequency (%)
02926
89.0%
1361
 
11.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number3287
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
02926
89.0%
1361
 
11.0%

Most occurring scripts

ValueCountFrequency (%)
Common3287
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
02926
89.0%
1361
 
11.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII3287
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
02926
89.0%
1361
 
11.0%

History
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size211.9 KiB
0
3177 
1
 
110

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3287
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
03177
96.7%
1110
 
3.3%

Length

2021-08-09T10:52:52.425285image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-08-09T10:52:52.503392image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
03177
96.7%
1110
 
3.3%

Most occurring characters

ValueCountFrequency (%)
03177
96.7%
1110
 
3.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number3287
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
03177
96.7%
1110
 
3.3%

Most occurring scripts

ValueCountFrequency (%)
Common3287
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
03177
96.7%
1110
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII3287
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
03177
96.7%
1110
 
3.3%

Action
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size211.9 KiB
0
2568 
1
719 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3287
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
02568
78.1%
1719
 
21.9%

Length

2021-08-09T10:52:52.706467image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-08-09T10:52:52.768955image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
02568
78.1%
1719
 
21.9%

Most occurring characters

ValueCountFrequency (%)
02568
78.1%
1719
 
21.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number3287
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
02568
78.1%
1719
 
21.9%

Most occurring scripts

ValueCountFrequency (%)
Common3287
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
02568
78.1%
1719
 
21.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII3287
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
02568
78.1%
1719
 
21.9%

Musical
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size211.9 KiB
0
3220 
1
 
67

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3287
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
03220
98.0%
167
 
2.0%

Length

2021-08-09T10:52:52.972032image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-08-09T10:52:53.034521image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
03220
98.0%
167
 
2.0%

Most occurring characters

ValueCountFrequency (%)
03220
98.0%
167
 
2.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number3287
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
03220
98.0%
167
 
2.0%

Most occurring scripts

ValueCountFrequency (%)
Common3287
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
03220
98.0%
167
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII3287
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
03220
98.0%
167
 
2.0%

Romance
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size211.9 KiB
0
2557 
1
730 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3287
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
02557
77.8%
1730
 
22.2%

Length

2021-08-09T10:52:53.249349image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-08-09T10:52:53.315262image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
02557
77.8%
1730
 
22.2%

Most occurring characters

ValueCountFrequency (%)
02557
77.8%
1730
 
22.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number3287
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
02557
77.8%
1730
 
22.2%

Most occurring scripts

ValueCountFrequency (%)
Common3287
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
02557
77.8%
1730
 
22.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII3287
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
02557
77.8%
1730
 
22.2%

Crime
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size211.9 KiB
0
2716 
1
571 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3287
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
02716
82.6%
1571
 
17.4%

Length

2021-08-09T10:52:53.492701image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-08-09T10:52:53.570807image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
02716
82.6%
1571
 
17.4%

Most occurring characters

ValueCountFrequency (%)
02716
82.6%
1571
 
17.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number3287
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
02716
82.6%
1571
 
17.4%

Most occurring scripts

ValueCountFrequency (%)
Common3287
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
02716
82.6%
1571
 
17.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII3287
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
02716
82.6%
1571
 
17.4%

Reality-TV
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size211.9 KiB
0
3287 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3287
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
03287
100.0%

Length

2021-08-09T10:52:53.759518image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-08-09T10:52:53.822015image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
03287
100.0%

Most occurring characters

ValueCountFrequency (%)
03287
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number3287
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
03287
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common3287
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
03287
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII3287
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
03287
100.0%

Family
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size211.9 KiB
0
2910 
1
377 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3287
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
02910
88.5%
1377
 
11.5%

Length

2021-08-09T10:52:54.009459image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-08-09T10:52:54.071947image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
02910
88.5%
1377
 
11.5%

Most occurring characters

ValueCountFrequency (%)
02910
88.5%
1377
 
11.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number3287
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
02910
88.5%
1377
 
11.5%

Most occurring scripts

ValueCountFrequency (%)
Common3287
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
02910
88.5%
1377
 
11.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII3287
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
02910
88.5%
1377
 
11.5%

News
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size211.9 KiB
0
3284 
1
 
3

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3287
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
03284
99.9%
13
 
0.1%

Length

2021-08-09T10:52:54.275020image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-08-09T10:52:54.353126image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
03284
99.9%
13
 
0.1%

Most occurring characters

ValueCountFrequency (%)
03284
99.9%
13
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number3287
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
03284
99.9%
13
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common3287
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
03284
99.9%
13
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII3287
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
03284
99.9%
13
 
0.1%

Music
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size211.9 KiB
0
3148 
1
 
139

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3287
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
03148
95.8%
1139
 
4.2%

Length

2021-08-09T10:52:54.540585image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-08-09T10:52:54.620539image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
03148
95.8%
1139
 
4.2%

Most occurring characters

ValueCountFrequency (%)
03148
95.8%
1139
 
4.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number3287
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
03148
95.8%
1139
 
4.2%

Most occurring scripts

ValueCountFrequency (%)
Common3287
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
03148
95.8%
1139
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII3287
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
03148
95.8%
1139
 
4.2%

Comedy
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size211.9 KiB
0
2020 
1
1267 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3287
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
02020
61.5%
11267
38.5%

Length

2021-08-09T10:52:54.811244image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-08-09T10:52:54.889351image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
02020
61.5%
11267
38.5%

Most occurring characters

ValueCountFrequency (%)
02020
61.5%
11267
38.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number3287
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
02020
61.5%
11267
38.5%

Most occurring scripts

ValueCountFrequency (%)
Common3287
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
02020
61.5%
11267
38.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII3287
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
02020
61.5%
11267
38.5%

Biography
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size211.9 KiB
0
3087 
1
 
200

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3287
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
03087
93.9%
1200
 
6.1%

Length

2021-08-09T10:52:55.076806image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-08-09T10:52:55.154913image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
03087
93.9%
1200
 
6.1%

Most occurring characters

ValueCountFrequency (%)
03087
93.9%
1200
 
6.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number3287
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
03087
93.9%
1200
 
6.1%

Most occurring scripts

ValueCountFrequency (%)
Common3287
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
03087
93.9%
1200
 
6.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII3287
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
03087
93.9%
1200
 
6.1%

Thriller
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size211.9 KiB
0
2331 
1
956 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3287
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
02331
70.9%
1956
29.1%

Length

2021-08-09T10:52:55.342369image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-08-09T10:52:55.420444image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
02331
70.9%
1956
29.1%

Most occurring characters

ValueCountFrequency (%)
02331
70.9%
1956
29.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number3287
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
02331
70.9%
1956
29.1%

Most occurring scripts

ValueCountFrequency (%)
Common3287
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
02331
70.9%
1956
29.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII3287
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
02331
70.9%
1956
29.1%

Game-Show
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size211.9 KiB
0
3287 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3287
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
03287
100.0%

Length

2021-08-09T10:52:55.592279image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-08-09T10:52:55.671540image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
03287
100.0%

Most occurring characters

ValueCountFrequency (%)
03287
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number3287
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
03287
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common3287
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
03287
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII3287
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
03287
100.0%

Documentary
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size211.9 KiB
0
3193 
1
 
94

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3287
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
03193
97.1%
194
 
2.9%

Length

2021-08-09T10:52:55.843374image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-08-09T10:52:55.921482image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
03193
97.1%
194
 
2.9%

Most occurring characters

ValueCountFrequency (%)
03193
97.1%
194
 
2.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number3287
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
03193
97.1%
194
 
2.9%

Most occurring scripts

ValueCountFrequency (%)
Common3287
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
03193
97.1%
194
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII3287
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
03193
97.1%
194
 
2.9%

Sport
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size211.9 KiB
0
3154 
1
 
133

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3287
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
03154
96.0%
1133
 
4.0%

Length

2021-08-09T10:52:56.108938image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-08-09T10:52:56.187045image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
03154
96.0%
1133
 
4.0%

Most occurring characters

ValueCountFrequency (%)
03154
96.0%
1133
 
4.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number3287
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
03154
96.0%
1133
 
4.0%

Most occurring scripts

ValueCountFrequency (%)
Common3287
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
03154
96.0%
1133
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII3287
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
03154
96.0%
1133
 
4.0%

Mystery
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size211.9 KiB
0
2949 
1
338 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3287
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
02949
89.7%
1338
 
10.3%

Length

2021-08-09T10:52:56.374501image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-08-09T10:52:56.436986image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
02949
89.7%
1338
 
10.3%

Most occurring characters

ValueCountFrequency (%)
02949
89.7%
1338
 
10.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number3287
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
02949
89.7%
1338
 
10.3%

Most occurring scripts

ValueCountFrequency (%)
Common3287
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
02949
89.7%
1338
 
10.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII3287
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
02949
89.7%
1338
 
10.3%

Short
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size211.9 KiB
0
3285 
1
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3287
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
03285
99.9%
12
 
0.1%

Length

2021-08-09T10:52:56.624442image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-08-09T10:52:56.702550image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
03285
99.9%
12
 
0.1%

Most occurring characters

ValueCountFrequency (%)
03285
99.9%
12
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number3287
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
03285
99.9%
12
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common3287
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
03285
99.9%
12
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII3287
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
03285
99.9%
12
 
0.1%

Animation
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size211.9 KiB
0
3115 
1
 
172

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3287
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
03115
94.8%
1172
 
5.2%

Length

2021-08-09T10:52:56.916234image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-08-09T10:52:56.983033image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
03115
94.8%
1172
 
5.2%

Most occurring characters

ValueCountFrequency (%)
03115
94.8%
1172
 
5.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number3287
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
03115
94.8%
1172
 
5.2%

Most occurring scripts

ValueCountFrequency (%)
Common3287
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
03115
94.8%
1172
 
5.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII3287
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
03115
94.8%
1172
 
5.2%

War
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size211.9 KiB
0
3185 
1
 
102

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3287
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
03185
96.9%
1102
 
3.1%

Length

2021-08-09T10:52:57.175775image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-08-09T10:52:57.253882image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
03185
96.9%
1102
 
3.1%

Most occurring characters

ValueCountFrequency (%)
03185
96.9%
1102
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number3287
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
03185
96.9%
1102
 
3.1%

Most occurring scripts

ValueCountFrequency (%)
Common3287
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
03185
96.9%
1102
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII3287
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
03185
96.9%
1102
 
3.1%

Western
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size211.9 KiB
0
3241 
1
 
46

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3287
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
03241
98.6%
146
 
1.4%

Length

2021-08-09T10:52:57.441338image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-08-09T10:52:57.519445image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
03241
98.6%
146
 
1.4%

Most occurring characters

ValueCountFrequency (%)
03241
98.6%
146
 
1.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number3287
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
03241
98.6%
146
 
1.4%

Most occurring scripts

ValueCountFrequency (%)
Common3287
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
03241
98.6%
146
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII3287
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
03241
98.6%
146
 
1.4%

Adventure
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size211.9 KiB
0
2705 
1
582 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3287
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
02705
82.3%
1582
 
17.7%

Length

2021-08-09T10:52:57.691278image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-08-09T10:52:57.769386image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
02705
82.3%
1582
 
17.7%

Most occurring characters

ValueCountFrequency (%)
02705
82.3%
1582
 
17.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number3287
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
02705
82.3%
1582
 
17.7%

Most occurring scripts

ValueCountFrequency (%)
Common3287
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
02705
82.3%
1582
 
17.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII3287
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
02705
82.3%
1582
 
17.7%

Sci-Fi
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size211.9 KiB
0
2900 
1
387 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3287
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
02900
88.2%
1387
 
11.8%

Length

2021-08-09T10:52:57.956841image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-08-09T10:52:58.019327image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
02900
88.2%
1387
 
11.8%

Most occurring characters

ValueCountFrequency (%)
02900
88.2%
1387
 
11.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number3287
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
02900
88.2%
1387
 
11.8%

Most occurring scripts

ValueCountFrequency (%)
Common3287
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
02900
88.2%
1387
 
11.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII3287
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
02900
88.2%
1387
 
11.8%

Drama
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size211.9 KiB
1
1665 
0
1622 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3287
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
11665
50.7%
01622
49.3%

Length

2021-08-09T10:52:58.222404image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-08-09T10:52:58.284890image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
11665
50.7%
01622
49.3%

Most occurring characters

ValueCountFrequency (%)
11665
50.7%
01622
49.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number3287
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
11665
50.7%
01622
49.3%

Most occurring scripts

ValueCountFrequency (%)
Common3287
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
11665
50.7%
01622
49.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII3287
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
11665
50.7%
01622
49.3%

Film-Noir
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size211.9 KiB
0
3287 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3287
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
03287
100.0%

Length

2021-08-09T10:52:58.472346image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-08-09T10:52:58.534831image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
03287
100.0%

Most occurring characters

ValueCountFrequency (%)
03287
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number3287
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
03287
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common3287
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
03287
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII3287
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
03287
100.0%

Fantasy
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size211.9 KiB
0
2900 
1
387 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3287
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
02900
88.2%
1387
 
11.8%

Length

2021-08-09T10:52:58.723492image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-08-09T10:52:58.785980image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
02900
88.2%
1387
 
11.8%

Most occurring characters

ValueCountFrequency (%)
02900
88.2%
1387
 
11.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number3287
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
02900
88.2%
1387
 
11.8%

Most occurring scripts

ValueCountFrequency (%)
Common3287
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
02900
88.2%
1387
 
11.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII3287
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
02900
88.2%
1387
 
11.8%

budget_y
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct341
Distinct (%)10.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35350373.79
Minimum0
Maximum380000000
Zeros549
Zeros (%)16.7%
Negative0
Negative (%)0.0%
Memory size51.4 KiB
2021-08-09T10:52:58.879707image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13000000
median20000000
Q350000000
95-th percentile140000000
Maximum380000000
Range380000000
Interquartile range (IQR)47000000

Descriptive statistics

Standard deviation45733691.98
Coefficient of variation (CV)1.293725839
Kurtosis5.442216495
Mean35350373.79
Median Absolute Deviation (MAD)19600000
Skewness2.132829134
Sum1.161966786 × 1011
Variance2.091570582 × 1015
MonotonicityNot monotonic
2021-08-09T10:52:59.004678image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0549
 
16.7%
20000000109
 
3.3%
30000000106
 
3.2%
4000000095
 
2.9%
2500000090
 
2.7%
1500000087
 
2.6%
3500000083
 
2.5%
1000000081
 
2.5%
5000000076
 
2.3%
6000000072
 
2.2%
Other values (331)1939
59.0%
ValueCountFrequency (%)
0549
16.7%
12
 
0.1%
31
 
< 0.1%
41
 
< 0.1%
61
 
< 0.1%
71
 
< 0.1%
91
 
< 0.1%
101
 
< 0.1%
151
 
< 0.1%
251
 
< 0.1%
ValueCountFrequency (%)
3800000001
 
< 0.1%
3000000001
 
< 0.1%
2700000001
 
< 0.1%
2600000001
 
< 0.1%
2580000002
 
0.1%
25000000010
0.3%
2450000001
 
< 0.1%
2370000001
 
< 0.1%
2250000003
 
0.1%
2200000001
 
< 0.1%

revenue
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct2456
Distinct (%)74.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean98073716.31
Minimum0
Maximum2847246203
Zeros764
Zeros (%)23.2%
Negative0
Negative (%)0.0%
Memory size51.4 KiB
2021-08-09T10:52:59.145271image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1102211.5
median30016165
Q3108271095.5
95-th percentile449294916.1
Maximum2847246203
Range2847246203
Interquartile range (IQR)108168884

Descriptive statistics

Standard deviation181876916.6
Coefficient of variation (CV)1.854491941
Kurtosis28.10643335
Mean98073716.31
Median Absolute Deviation (MAD)30016165
Skewness4.077265642
Sum3.223683055 × 1011
Variance3.307921278 × 1016
MonotonicityNot monotonic
2021-08-09T10:52:59.270241image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0764
 
23.2%
5623634493
 
0.1%
1640000003
 
0.1%
3688710073
 
0.1%
70000003
 
0.1%
2635914152
 
0.1%
563088812
 
0.1%
41000002
 
0.1%
475369592
 
0.1%
823942882
 
0.1%
Other values (2446)2501
76.1%
ValueCountFrequency (%)
0764
23.2%
11
 
< 0.1%
71
 
< 0.1%
82
 
0.1%
151
 
< 0.1%
251
 
< 0.1%
321
 
< 0.1%
701
 
< 0.1%
1031
 
< 0.1%
1261
 
< 0.1%
ValueCountFrequency (%)
28472462031
< 0.1%
16717132081
< 0.1%
15188155151
< 0.1%
15150476711
< 0.1%
14054036941
< 0.1%
12742190091
< 0.1%
12148112521
< 0.1%
11567309621
< 0.1%
11532962931
< 0.1%
11188889791
< 0.1%

profit
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct2570
Distinct (%)78.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean62723342.53
Minimum-150000000
Maximum2610246203
Zeros435
Zeros (%)13.2%
Negative949
Negative (%)28.9%
Memory size51.4 KiB
2021-08-09T10:52:59.410833image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-150000000
5-th percentile-23397435.8
Q1-1000000
median6233280
Q365987425
95-th percentile338616874.5
Maximum2610246203
Range2760246203
Interquartile range (IQR)66987425

Descriptive statistics

Standard deviation150104267.6
Coefficient of variation (CV)2.393116526
Kurtosis40.17131279
Mean62723342.53
Median Absolute Deviation (MAD)17700053
Skewness4.746164146
Sum2.061716269 × 1011
Variance2.253129114 × 1016
MonotonicityNot monotonic
2021-08-09T10:52:59.535803image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0435
 
13.2%
-1000000026
 
0.8%
-100000020
 
0.6%
-300000013
 
0.4%
-50000010
 
0.3%
-200000010
 
0.3%
-70000009
 
0.3%
-120000008
 
0.2%
-50000008
 
0.2%
-35000007
 
0.2%
Other values (2560)2741
83.4%
ValueCountFrequency (%)
-1500000001
< 0.1%
-1191800391
< 0.1%
-1110072421
< 0.1%
-1000000001
< 0.1%
-983011011
< 0.1%
-928960271
< 0.1%
-840000001
< 0.1%
-800000002
0.1%
-796277091
< 0.1%
-728220071
< 0.1%
ValueCountFrequency (%)
26102462031
< 0.1%
15217132081
< 0.1%
13250476711
< 0.1%
12988155151
< 0.1%
11554036941
< 0.1%
11242190091
< 0.1%
10827309621
< 0.1%
10248889791
< 0.1%
10148112521
< 0.1%
9085610132
0.1%

Interactions

2021-08-09T10:51:44.908905image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T10:51:45.115668image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T10:51:45.246217image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T10:51:45.386810image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T10:51:45.511778image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T10:51:45.667992image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T10:51:45.808585image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T10:51:45.964799image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T10:51:46.105388image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T10:51:46.245605image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T10:51:46.376133image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T10:51:46.515310image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T10:51:46.766373image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T10:51:46.922591image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T10:51:47.063181image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T10:51:47.203774image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T10:51:47.359983image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T10:51:47.500580image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T10:51:47.641185image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T10:51:47.766137image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T10:51:47.906255image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T10:51:48.021178image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T10:51:48.130527image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T10:51:48.239876image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T10:51:48.364849image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T10:51:48.489818image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T10:51:48.614788image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T10:51:48.740944image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T10:51:48.853862image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T10:51:48.971585image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T10:51:49.158144image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T10:51:49.282458image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T10:51:49.413013image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T10:51:49.522360image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T10:51:49.647331image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T10:51:49.772302image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T10:51:49.897272image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T10:51:50.023439image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T10:51:50.132790image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T10:51:50.273383image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T10:51:50.382733image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T10:51:50.492047image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T10:51:50.600431image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T10:51:50.730647image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T10:51:50.855618image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T10:51:51.089940image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T10:51:51.241014image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T10:51:51.354586image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T10:51:51.469207image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T10:51:51.594178image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T10:51:51.703526image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T10:51:51.844119image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T10:51:51.968251image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T10:51:52.082872image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T10:51:52.223464image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T10:51:52.348435image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T10:51:52.457784image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T10:51:52.582756image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T10:51:52.707725image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T10:51:52.832697image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T10:51:52.942046image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T10:51:53.051395image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T10:51:53.176366image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T10:51:53.316058image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T10:51:53.451220image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T10:51:53.573220image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T10:51:53.720247image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T10:51:53.829638image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T10:51:53.954607image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T10:51:54.079539image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T10:51:54.212874image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T10:51:54.367348image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T10:51:54.492320image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T10:51:54.620457image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T10:51:54.741904image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T10:51:54.856442image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T10:51:54.971328image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T10:51:55.111920image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T10:51:55.236889image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T10:51:55.346240image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T10:51:55.471214image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T10:51:55.611802image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T10:51:55.738005image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T10:51:55.878595image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T10:51:56.013224image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T10:51:56.133600image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T10:51:56.413718image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T10:51:56.555424image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T10:51:56.695047image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T10:51:56.825627image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T10:51:56.966185image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T10:51:57.106777image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T10:51:57.231747image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T10:51:57.387265image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T10:51:57.517494image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T10:51:57.642466image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T10:51:57.783058image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T10:51:57.908027image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T10:51:58.048620image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T10:51:58.173591image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T10:51:58.314183image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T10:51:58.454775image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T10:51:58.594928image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T10:51:58.784645image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T10:51:58.925243image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T10:51:59.071372image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T10:51:59.211964image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T10:51:59.336931image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T10:51:59.493110image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T10:51:59.618114image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T10:51:59.758709image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T10:51:59.899265image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T10:52:00.056315image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T10:52:00.186561image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T10:52:00.311531image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T10:52:00.467745image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T10:52:00.608337image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T10:52:00.733308image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T10:52:00.858278image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T10:52:01.027789image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T10:52:01.160136image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T10:52:01.316351image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T10:52:01.456192image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T10:52:01.599964image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T10:52:01.740556image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T10:52:01.881148image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T10:52:02.037361image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T10:52:02.177954image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T10:52:02.318546image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T10:52:02.459136image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T10:52:02.615351image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T10:52:02.755942image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T10:52:02.927094image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T10:52:03.072966image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T10:52:03.401016image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T10:52:03.541607image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T10:52:03.666578image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T10:52:03.825360image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T10:52:03.959185image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T10:52:04.130969image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T10:52:04.271051image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T10:52:04.401596image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T10:52:04.542189image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T10:52:04.667157image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T10:52:04.792130image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T10:52:04.932720image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T10:52:05.068863image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
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2021-08-09T10:52:32.249643image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T10:52:32.390240image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T10:52:32.515210image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T10:52:32.640177image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T10:52:32.780769image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T10:52:32.905740image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T10:52:33.030712image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T10:52:33.155680image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T10:52:33.296274image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T10:52:33.767234image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T10:52:33.919915image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T10:52:34.083623image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T10:52:34.224213image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T10:52:34.349186image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-09T10:52:34.474151image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Correlations

2021-08-09T10:52:59.723260image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-08-09T10:53:00.354575image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-08-09T10:53:00.963807image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-08-09T10:53:01.588661image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-08-09T10:53:02.171314image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-08-09T10:52:34.832369image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
A simple visualization of nullity by column.
2021-08-09T10:52:40.817725image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2021-08-09T10:52:42.705160image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2021-08-09T10:52:43.315444image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

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0ColorDan Trachtenberg411.0104.016.082.0John Gallagher Jr.14000.071897215.0Drama|Horror|Mystery|Sci-Fi|ThrillerBradley Cooper10 Cloverfield Lane12689314504Sumalee Montano0.0alien|bunker|car crash|kidnapping|minimal casthttp://www.imdb.com/title/tt1179933/?ref_=fn_tt_tt_1440.0EnglishUSAPG-1315000000.02016.0338.07.32.3533000100000000000100010000011001500000010828642193286421
1ColorTimothy Hines1.0111.00.0247.0Kelly LeBrock1000.014616.0DramaChristopher Lambert10 Days in a Madhouse3142059Alexandra Callas1.0NaNhttp://www.imdb.com/title/tt3453052/?ref_=fn_tt_tt_110.0EnglishUSAR12000000.02015.0445.07.51.85260000000000000000000000000010012000000-1200000
2ColorGil Junger133.097.019.0835.0Heath Ledger23000.038176108.0Comedy|Drama|RomanceJoseph Gordon-Levitt10 Things I Hate About You22209937907Andrew Keegan6.0dating|protective father|school|shrew|teen moviehttp://www.imdb.com/title/tt0147800/?ref_=fn_tt_tt_1549.0EnglishUSAPG-1316000000.01999.013000.07.21.851000000001000001000000000000100160000005347816637478166
3ColorKevin Lima84.0100.036.0439.0Eric Idle2000.066941559.0Adventure|Comedy|FamilyIoan Gruffudd102 Dalmatians264134182Jim Carter1.0dog|parole|parole officer|prison|puppyhttp://www.imdb.com/title/tt0211181/?ref_=fn_tt_tt_177.0EnglishUSAG85000000.02000.0795.04.81.85372000000010010000000000100008500000018361177198611771
4ColorRobert Moresco26.0107.053.0463.0Brad Renfro954.053481.0Crime|Drama|ThrillerBrian Dennehy10th & Wolf55572512Dash Mihok5.0desert storm|fbi|fbi agent|fragmentation grenade|woman kills attackerhttp://www.imdb.com/title/tt0360323/?ref_=fn_tt_tt_134.0EnglishUSAR8000000.02006.0551.06.42.35294000001000000100000000001008000000143451-7856549
5ColorGreg Marcks68.085.09.0407.0Barbara Hershey861.0NaNComedy|Crime|DramaHenry Thomas11:14382732200Shawn Hatosy1.0convenience store|multiple perspectives|murder|paramedic|vanhttp://www.imdb.com/title/tt0331811/?ref_=fn_tt_tt_1133.0EnglishUSAR6000000.02003.0618.07.21.8500000010000100000000000010060000000-6000000
6ColorRenny Harlin113.0108.0212.0347.0Ashley Scott969.012232937.0Action|Crime|ThrillerTaylor Cole12 Rounds228232799Nick Gomez1.02000s|detective|sadist|terrorism|terroristhttp://www.imdb.com/title/tt1160368/?ref_=fn_tt_tt_1113.0EnglishUSAPG-1322000000.02009.0794.05.62.350001001000000100000000000002000000017280326-2719674
7ColorSteve McQueen597.0134.00.0500.0Scoot McNairy2000.056667870.0Biography|Drama|HistoryQuvenzhané Wallis12 Years a Slave4391764251Taran Killam0.0racism|separation from family|social injustice|torture|whippinghttp://www.imdb.com/title/tt2024544/?ref_=fn_tt_tt_1695.0EnglishUSAR20000000.02013.0660.08.12.35830000100000000010000000000010020000000187000000167000000
8ColorDanny Boyle450.094.00.0223.0Treat Williams11000.018329466.0Adventure|Biography|Drama|ThrillerJames Franco127 Hours27917911984Kate Burton0.0alone|aron ralston|based on autobiography|canyon|survivalhttp://www.imdb.com/title/tt1542344/?ref_=fn_tt_tt_1440.0EnglishUSAR18000000.02010.0642.07.61.856300000000000000110000000010100180000003569292017692920
9ColorGary Winick119.098.056.0533.0Judy Greer3000.056044241.0Comedy|Fantasy|RomanceJennifer Garner13 Going on 301221876742Christa B. Allen1.013 year old|13th birthday|30 year old|wish|year 1987http://www.imdb.com/title/tt0337563/?ref_=fn_tt_tt_1339.0EnglishUSAPG-1337000000.02004.02000.06.11.85000001000001000000000000001370000009645569759455697

Last rows

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3277ColorMora Stephens35.0103.05.0842.0Alexandra Breckenridge1000.0NaNDrama|ThrillerRay WinstoneZipper40913408Elena Satine0.0escort|f word|no opening credits|one word title|prosecutorhttp://www.imdb.com/title/tt3346224/?ref_=fn_tt_tt_120.0EnglishUSAR4500000.02015.01000.05.72.3598700000000000010000000000100000
3278ColorKevin Hamedani64.089.023.023.0Janette Armand199.0NaNComedy|Horror|Sci-FiRussell HodgkinsonZMD: Zombies of Mass Destruction3650292Kevin Hamedani0.0cult|homosexual|island|survival horror|zombiehttp://www.imdb.com/title/tt1134674/?ref_=fn_tt_tt_139.0EnglishUSAR500000.02009.037.05.11.85010000000001000000000001000000
3279ColorDavid Fincher377.0162.021000.0495.0Jake Gyllenhaal21000.033048353.0Crime|Drama|History|Mystery|ThrillerRobert Downey Jr.Zodiac30127936928Anthony Edwards0.0cartoonist|reporter|serial killer|zodiac|zodiac killerhttp://www.imdb.com/title/tt0443706/?ref_=fn_tt_tt_1589.0EnglishUSAR65000000.02007.015000.07.72.351200001000100000010001000000100650000008478591419785914
3280ColorK. King150.093.03.0115.0Shona Kay214.0NaNAction|Comedy|HorrorJason K. WixomZombie Hunter2057656Jarrod Phillips2.0desert|drifter|seduction|siege|zombiehttp://www.imdb.com/title/tt2446502/?ref_=fn_tt_tt_130.0EnglishUSANot Rated1000000.02013.0211.03.52.35010100000001000000000000000000
3281ColorRuben Fleischer445.088.0181.011.0Bill Murray15000.075590286.0Adventure|Comedy|Horror|Sci-FiEmma StoneZombieland38621728011Derek Graf4.0amusement park|on the road|zombie|zombie apocalypse|zombie spoofhttp://www.imdb.com/title/tt1156398/?ref_=fn_tt_tt_1553.0EnglishUSAR23600000.02009.013000.07.72.3526000100000000010000000000110002360000010239154078791540
3282ColorFrank Coraci178.0102.0153.0269.0Leslie Bibb3000.080360866.0Comedy|Family|RomanceRosario DawsonZookeeper446625392Nicholas Turturro1.0champagne bottle|coca cola|jewelry box|red bull|zoohttp://www.imdb.com/title/tt1222817/?ref_=fn_tt_tt_1127.0EnglishUSAPG80000000.02011.01000.05.22.350000010010010000000000000008000000016985275989852759
3283ColorBen Stiller226.0102.00.01000.0Will Ferrell14000.028837115.0ComedyMilla JovovichZoolander 23496424107Justin Theroux4.0chosen one|fashion|fashion model|model|retiredhttp://www.imdb.com/title/tt1608290/?ref_=fn_tt_tt_1150.0EnglishUSAPG-1350000000.02016.08000.04.82.35280000000000000100000000000000050000000559690005969000
3284ColorBen Stiller135.090.00.08000.0Alexander Skarsgård14000.045162741.0ComedyMilla JovovichZoolander20108434565Will Ferrell0.0fashion|malaysia|male model|reporter|rivalhttp://www.imdb.com/title/tt0196229/?ref_=fn_tt_tt_1523.0EnglishGermanyPG-1328000000.02001.010000.06.62.35000000000001000000000000000280000006078098132780981
3285ColorPeter Hewitt63.083.012.0690.0Rip Torn2000.011631245.0Action|Adventure|Family|Sci-FiKevin ZegersZoom150155022Thomas F. Wilson5.0bruise|female hero|super strength|superhero|teenage superherohttp://www.imdb.com/title/tt0383060/?ref_=fn_tt_tt_1113.0EnglishUSAPG35000000.02006.0826.04.21.85494001000010000000000000110007560000012506188-63093812
3286ColorJérôme Salle69.0110.022.044.0Tanya van Graan5000.0NaNCrime|Drama|ThrillerOrlando BloomZulu128175273Conrad Kemp0.0apartheid|corpse|male nudity|murder|police officerhttp://www.imdb.com/title/tt2249221/?ref_=fn_tt_tt_143.0EnglishFranceR16000000.02013.0170.06.72.35000000100000010000000000100350000080000004500000